diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial.rst b/docs/source/_rst/tutorials/tutorial1/tutorial.rst index 54ce660..b0ed833 100644 --- a/docs/source/_rst/tutorials/tutorial1/tutorial.rst +++ b/docs/source/_rst/tutorials/tutorial1/tutorial.rst @@ -14,13 +14,13 @@ a toy problem, following the standard API procedure. Specifically, the tutorial aims to introduce the following topics: -- Explaining how to build **PINA** Problem, -- Showing how to generate data for ``PINN`` straining +- Explaining how to build **PINA** Problems, +- Showing how to generate data for ``PINN`` training These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary -Differential Equation (ODE) problem busing the ``PINN`` solver. +Differential Equation (ODE) problem using the ``PINN`` solver. Build a PINA problem -------------------- @@ -66,9 +66,8 @@ the tensor. The ``spatial_domain`` variable indicates where the sample points are going to be sampled in the domain, in this case :math:`x\in[0,1]`. -What about if our equation is also time dependent? In this case, our -``class`` will inherit from both ``SpatialProblem`` and -``TimeDependentProblem``: +What if our equation is also time-dependent? In this case, our ``class`` +will inherit from both ``SpatialProblem`` and ``TimeDependentProblem``: .. code:: ipython3 @@ -83,6 +82,13 @@ What about if our equation is also time dependent? In this case, our # other stuff ... + +.. parsed-literal:: + + Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions. + Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions. + + where we have included the ``temporal_domain`` variable, indicating the time domain wanted for the solution. @@ -157,7 +163,7 @@ returning the difference between subtracting the variable ``u`` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a ``python`` function, but an ``Equation`` object, which is initialized with the -``python`` function. This is done so that all the computations, and +``python`` function. This is done so that all the computations and internal checks are done inside **PINA**. Once we have defined the function, we need to tell the neural network @@ -169,25 +175,25 @@ possibilities are allowed, see the documentation for reference). Finally, it’s possible to define a ``truth_solution`` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the -``truth_solution`` function is a method of the ``PINN`` class, but is +``truth_solution`` function is a method of the ``PINN`` class, but it is not mandatory for problem definition. Generate data ------------- Data for training can come in form of direct numerical simulation -reusults, or points in the domains. In case we do unsupervised learning, -we just need the collocation points for training, i.e. points where we -want to evaluate the neural network. Sampling point in **PINA** is very -easy, here we show three examples using the ``.discretise_domain`` -method of the ``AbstractProblem`` class. +results, or points in the domains. In case we perform unsupervised +learning, we just need the collocation points for training, i.e. points +where we want to evaluate the neural network. Sampling point in **PINA** +is very easy, here we show three examples using the +``.discretise_domain`` method of the ``AbstractProblem`` class. .. code:: ipython3 # sampling 20 points in [0, 1] through discretization in all locations problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') - # sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0 + # sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0 problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) @@ -301,11 +307,13 @@ If you want to track the metric by yourself without a logger, use TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs + /Users/alessio/opt/anaconda3/envs/pina/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default + Missing logger folder: /Users/alessio/Downloads/lightning_logs .. parsed-literal:: - Epoch 1499: : 1it [00:00, 272.55it/s, v_num=3, x0_loss=7.71e-6, D_loss=0.000734, mean_loss=0.000371] + Epoch 1499: | | 1/? [00:00<00:00, 167.08it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401] .. parsed-literal:: @@ -314,7 +322,7 @@ If you want to track the metric by yourself without a logger, use .. parsed-literal:: - Epoch 1499: : 1it [00:00, 167.14it/s, v_num=3, x0_loss=7.71e-6, D_loss=0.000734, mean_loss=0.000371] + Epoch 1499: | | 1/? [00:00<00:00, 102.49it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401] After the training we can inspect trainer logged metrics (by default @@ -332,8 +340,8 @@ loss can be accessed by ``trainer.logged_metrics`` .. parsed-literal:: - {'x0_loss': tensor(7.7149e-06), - 'D_loss': tensor(0.0007), + {'x0_loss': tensor(1.0674e-05), + 'D_loss': tensor(0.0008), 'mean_loss': tensor(0.0004)} @@ -347,8 +355,13 @@ quatitative plots of the solution. pl.plot(solver=pinn) +.. parsed-literal:: -.. image:: tutorial_files/tutorial_23_0.png + Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions. + + + +.. image:: tutorial_files/tutorial_23_1.png @@ -375,14 +388,16 @@ could train for longer What’s next? ------------ -Nice you have completed the introductory tutorial of **PINA**! There are -multiple directions you can go now: +Congratulations on completing the introductory tutorial of **PINA**! +There are several directions you can go now: 1. Train the network for longer or with different layer sizes and assert the finaly accuracy 2. Train the network using other types of models (see ``pina.model``) -3. GPU trainining and benchmark the speed +3. GPU training and speed benchmarking 4. Many more… + + diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png index b9d7a7d..3c90635 100644 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png deleted file mode 100644 index ab1bd1d..0000000 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png new file mode 100644 index 0000000..e4d92c2 Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_1.png differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png index 7083730..64bd43a 100644 Binary files a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png differ diff --git a/pina/meta.py b/pina/meta.py index efad679..57fefbb 100644 --- a/pina/meta.py +++ b/pina/meta.py @@ -13,7 +13,7 @@ __all__ = [ __project__ = "PINA" __title__ = "pina" __author__ = "PINA Contributors" -__copyright__ = "Copyright 2021-2024, PINA Contributors" +__copyright__ = "2021-2024, PINA Contributors" __license__ = "MIT" __version__ = "0.1.0" __mail__ = "demo.nicola@gmail.com, dario.coscia@sissa.it" # TODO diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb index c56beed..61453f4 100644 --- a/tutorials/tutorial1/tutorial.ipynb +++ b/tutorials/tutorial1/tutorial.ipynb @@ -23,10 +23,10 @@ "\n", "Specifically, the tutorial aims to introduce the following topics:\n", "\n", - "* Explaining how to build **PINA** Problem,\n", - "* Showing how to generate data for `PINN` straining\n", + "* Explaining how to build **PINA** Problems,\n", + "* Showing how to generate data for `PINN` training\n", "\n", - "These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem busing the `PINN` solver." + "These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem using the `PINN` solver." ] }, { @@ -73,7 +73,7 @@ "\n", "Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$), this is done because in **PINA** the `torch.Tensor`s are labelled, allowing the user maximal flexibility for the manipulation of the tensor. The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\\in[0,1]$.\n", "\n", - "What about if our equation is also time dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:\n" + "What if our equation is also time-dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:\n" ] }, { @@ -81,7 +81,16 @@ "execution_count": 1, "id": "2373a925", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + } + ], "source": [ "from pina.problem import SpatialProblem, TimeDependentProblem\n", "from pina.geometry import CartesianDomain\n", @@ -176,11 +185,11 @@ "id": "7cf64d01", "metadata": {}, "source": [ - "After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations, and internal checks are done inside **PINA**.\n", + "After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations and internal checks are done inside **PINA**.\n", "\n", "Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference).\n", "\n", - "Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but is not mandatory for problem definition.\n" + "Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but it is not mandatory for problem definition.\n" ] }, { @@ -191,7 +200,7 @@ "source": [ "## Generate data \n", "\n", - "Data for training can come in form of direct numerical simulation reusults, or points in the domains. In case we do unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class." + "Data for training can come in form of direct numerical simulation results, or points in the domains. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class." ] }, { @@ -204,7 +213,7 @@ "# sampling 20 points in [0, 1] through discretization in all locations\n", "problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all')\n", "\n", - "# sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0\n", + "# sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0\n", "problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D'])\n", "problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0'])\n", "\n", @@ -289,13 +298,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "33cc80bc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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DAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGohiZ0VK1Zo4MCBio+PV2Zmpj7++OPLjl+/fr2GDRum+Ph4jRw5UiUlJa2+bozRkiVLlJycrB49eig7O1t/+MMfgnkIAAAgQgU9dtatW6f8/HwVFBSosrJSo0aNUk5Ojqqrq9scv3XrVk2ZMkUzZszQ9u3blZubq9zcXO3atcs/5qmnntLy5cu1atUqffTRR7rqqquUk5Oj8+fPB/twruhz3zltPXBSn/vOXfxF3zHp0PvN/3a2YN53uOYSyP0E6/i7yrqGch62Pk9D/byMxHXsKs/3Kwnn9yBSfxYjdd6dxGGMMcF8gMzMTN1888168cUXJUlNTU1KTU3VnDlzNH/+/IvGT548WWfPntXGjRv928aOHau0tDStWrVKxhilpKRo3rx5+v73vy9J8vl8SkpKUnFxsb797W9fcU41NTVyu93y+XxyuVyddKTSum1HtGDDTjUZKcohFd49UpNvHtD8xcrXpHcekUyT5IiS7nxeGj2tcx44mPcdrrkEcj/BOv6usq6hnIetz9NQPy8jcR27yvP9SsL5PYjUn8VInXcbOvr6HdQzO/X19aqoqFB2dvYXDxgVpezsbJWXl7e5T3l5eavxkpSTk+Mff+jQIXm93lZj3G63MjMzL3mfdXV1qqmpaXXrbJ/7zvlDR5KajLRww67mMzy+Y19886Xmf995tPNqPVj3Ha65BHI/wTr+rrKuoZyHrc/TUD8vI3Edu8rz/UrC+T2I1J/FSJ13Jwtq7Jw8eVKNjY1KSkpqtT0pKUler7fNfbxe72XHt/wbyH0WFhbK7Xb7b6mpqR06nss5dPKsP3RaNBqjwydrpVMHvvjmtzCN0qmDf/0DB/O+wzWXQO4nWMffVdY1lPOw9Xka6udlJK5jV3m+X0k4vweR+rMYqfPuZN3i01gLFiyQz+fz344ePdrpjzGo71WKcrTeFu1waGDfnlLvIc2n8/6SI1rqPfivf+Bg3ne45hLI/QTr+LvKuoZyHrY+T0P9vIzEdewqz/crCef3IFJ/FiN13p0sqLHTt29fRUdHq6qqqtX2qqoqeTyeNvfxeDyXHd/ybyD36XQ65XK5Wt06W7K7hwrvHqloR3PxRDscWnr3CCW7e0ju/s3vWzqimwc7oqU7i5q3/7WCed/hmksg9xOs4+8q6xrKedj6PA318zIS17GrPN+vJJzfg0j9WYzUeXeykFygnJGRoRdeeEFS8wXKAwYM0OzZsy95gXJtba3eeecd/7Zx48bppptuanWB8ve//33NmzdPUvMFS4mJiWG/QFlqvnbn8MlaDezbszl0/pLvWPPpvN6DO/+bH8z7DtdcArmfYB1/V1nXUM7D1udpqJ+XkbiOXeX5fiXh/B5E6s9ipM77Szr8+m2CbO3atcbpdJri4mKzZ88eM3PmTJOQkGC8Xq8xxpi8vDwzf/58//gPP/zQxMTEmKefftrs3bvXFBQUmNjYWLNz507/mGXLlpmEhATzH//xH+a///u/zV133WUGDRpkzp071645+Xw+I8n4fL7OPVgAABA0HX39junU5GrD5MmTdeLECS1ZskRer1dpaWkqLS31X2B85MgRRUV98W7auHHjtGbNGi1evFgLFy7Uddddp7ffflsjRozwj3n88cd19uxZzZw5U6dPn9bXvvY1lZaWKj4+PtiHAwAAIkzQ38bqioL5NhYAAAiOLvl7dgAAAMKN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYLWixc+rUKU2dOlUul0sJCQmaMWOG/vznP192n/Pnz2vWrFnq06ePrr76ak2aNElVVVX+r//+97/XlClTlJqaqh49emj48OF6/vnng3UIAADAAkGLnalTp2r37t3atGmTNm7cqPfff18zZ8687D5z587VO++8o/Xr1+s3v/mNjh8/rrvvvtv/9YqKCiUmJuqNN97Q7t27tWjRIi1YsEAvvvhisA4DAABEOIcxxnT2ne7du1c33HCDtm3bpvT0dElSaWmpJk6cqM8++0wpKSkX7ePz+dSvXz+tWbNG99xzjyRp3759Gj58uMrLyzV27Ng2H2vWrFnau3evNm/efMn51NXVqa6uzv/fNTU1Sk1Nlc/nk8vl+msOFQAAhEhNTY3cbnfAr99BObNTXl6uhIQEf+hIUnZ2tqKiovTRRx+1uU9FRYUaGhqUnZ3t3zZs2DANGDBA5eXll3wsn8+n3r17X3Y+hYWFcrvd/ltqamqARwQAACJVUGLH6/UqMTGx1baYmBj17t1bXq/3kvvExcUpISGh1fakpKRL7rN161atW7fuim+PLViwQD6fz387evRo+w8GAABEtIBiZ/78+XI4HJe97du3L1hzbWXXrl266667VFBQoL/927+97Fin0ymXy9XqBgAAuoeYQAbPmzdP999//2XHDB48WB6PR9XV1a22X7hwQadOnZLH42lzP4/Ho/r6ep0+fbrV2Z2qqqqL9tmzZ48mTJigmTNnavHixYEcAgAA6GYCip1+/fqpX79+VxyXlZWl06dPq6KiQmPGjJEkbd68WU1NTcrMzGxznzFjxig2NlZlZWWaNGmSJGn//v06cuSIsrKy/ON2796t2267TdOnT9dPfvKTQKYPAAC6oaB8GkuS/u7v/k5VVVVatWqVGhoa9MADDyg9PV1r1qyRJB07dkwTJkzQa6+9poyMDEnSP/zDP6ikpETFxcVyuVyaM2eOpOZrc6Tmt65uu+025eTk6Kc//an/saKjo9sVYS06ejU3AAAIn46+fgd0ZicQq1ev1uzZszVhwgRFRUVp0qRJWr58uf/rDQ0N2r9/v2pra/3bnnvuOf/Yuro65eTk6KWXXvJ//c0339SJEyf0xhtv6I033vBv/8pXvqLDhw8H61AAAEAEC9qZna6MMzsAAESeLvV7dgAAALoKYgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YIWO6dOndLUqVPlcrmUkJCgGTNm6M9//vNl9zl//rxmzZqlPn366Oqrr9akSZNUVVXV5tj//d//1bXXXiuHw6HTp08H4QgAAIANghY7U6dO1e7du7Vp0yZt3LhR77//vmbOnHnZfebOnat33nlH69ev129+8xsdP35cd999d5tjZ8yYoZtuuikYUwcAABZxGGNMZ9/p3r17dcMNN2jbtm1KT0+XJJWWlmrixIn67LPPlJKSctE+Pp9P/fr105o1a3TPPfdIkvbt26fhw4ervLxcY8eO9Y9duXKl1q1bpyVLlmjChAn605/+pISEhEvOp66uTnV1df7/rqmpUWpqqnw+n1wuVycdNQAACKaamhq53e6AX7+DcmanvLxcCQkJ/tCRpOzsbEVFRemjjz5qc5+Kigo1NDQoOzvbv23YsGEaMGCAysvL/dv27NmjH/3oR3rttdcUFdW+6RcWFsrtdvtvqampHTwyAAAQaYISO16vV4mJia22xcTEqHfv3vJ6vZfcJy4u7qIzNElJSf596urqNGXKFP30pz/VgAED2j2fBQsWyOfz+W9Hjx4N7IAAAEDECih25s+fL4fDcdnbvn37gjVXLViwQMOHD9d3vvOdgPZzOp1yuVytbgAAoHuICWTwvHnzdP/99192zODBg+XxeFRdXd1q+4ULF3Tq1Cl5PJ429/N4PKqvr9fp06dbnd2pqqry77N582bt3LlTb775piSp5XKjvn37atGiRfqnf/qnQA4HAAB0AwHFTr9+/dSvX78rjsvKytLp06dVUVGhMWPGSGoOlaamJmVmZra5z5gxYxQbG6uysjJNmjRJkrR//34dOXJEWVlZkqR///d/17lz5/z7bNu2Td/97nf1wQcfaMiQIYEcCgAA6CYCip32Gj58uG6//XY99NBDWrVqlRoaGjR79mx9+9vf9n8S69ixY5owYYJee+01ZWRkyO12a8aMGcrPz1fv3r3lcrk0Z84cZWVl+T+J9eWgOXnypP/xLvdpLAAA0H0FJXYkafXq1Zo9e7YmTJigqKgoTZo0ScuXL/d/vaGhQfv371dtba1/23PPPecfW1dXp5ycHL300kvBmiIAAOgGgvJ7drq6jn5OHwAAhE+X+j07AAAAXQWxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsFhPuCYSDMUaSVFNTE+aZAACA9mp53W55HW+vbhk7Z86ckSSlpqaGeSYAACBQZ86ckdvtbvd4hwk0jyzQ1NSk48ePq1evXnI4HJ163zU1NUpNTdXRo0flcrk69b7RNtY89Fjz0GPNQ481D70rrbkxRmfOnFFKSoqiotp/JU63PLMTFRWla6+9NqiP4XK5+OEIMdY89Fjz0GPNQ481D73LrXkgZ3RacIEyAACwGrEDAACsRux0MqfTqYKCAjmdznBPpdtgzUOPNQ891jz0WPPQC9aad8sLlAEAQPfBmR0AAGA1YgcAAFiN2AEAAFYjdgAAgNWInQCtWLFCAwcOVHx8vDIzM/Xxxx9fdvz69es1bNgwxcfHa+TIkSopKQnRTO0SyLq/8sor+vrXv65rrrlG11xzjbKzs6/4fcLFAn2ut1i7dq0cDodyc3ODO0ELBbrmp0+f1qxZs5ScnCyn06nrr7+e/8cEKNA1Lyoq0tChQ9WjRw+lpqZq7ty5On/+fIhmG/nef/993XnnnUpJSZHD4dDbb799xX22bNmi0aNHy+l06qtf/aqKi4sDf2CDdlu7dq2Ji4szr776qtm9e7d56KGHTEJCgqmqqmpz/Icffmiio6PNU089Zfbs2WMWL15sYmNjzc6dO0M888gW6Lrfd999ZsWKFWb79u1m79695v777zdut9t89tlnIZ555Ap0zVscOnTI9O/f33z96183d911V2gma4lA17yurs6kp6ebiRMnmt/+9rfm0KFDZsuWLWbHjh0hnnnkCnTNV69ebZxOp1m9erU5dOiQ+dWvfmWSk5PN3LlzQzzzyFVSUmIWLVpkNmzYYCSZt95667LjDx48aHr27Gny8/PNnj17zAsvvGCio6NNaWlpQI9L7AQgIyPDzJo1y//fjY2NJiUlxRQWFrY5/t577zV33HFHq22ZmZnm7//+74M6T9sEuu5fduHCBdOrVy/z85//PFhTtE5H1vzChQtm3Lhx5mc/+5mZPn06sROgQNd85cqVZvDgwaa+vj5UU7ROoGs+a9Ysc9ttt7Xalp+fb2655ZagztNW7Ymdxx9/3Nx4442ttk2ePNnk5OQE9Fi8jdVO9fX1qqioUHZ2tn9bVFSUsrOzVV5e3uY+5eXlrcZLUk5OziXH42IdWfcvq62tVUNDg3r37h2saVqlo2v+ox/9SImJiZoxY0YopmmVjqz5L3/5S2VlZWnWrFlKSkrSiBEjtHTpUjU2NoZq2hGtI2s+btw4VVRU+N/qOnjwoEpKSjRx4sSQzLk76qzX0W75h0A74uTJk2psbFRSUlKr7UlJSdq3b1+b+3i93jbHe73eoM3TNh1Z9y974oknlJKSctEPDNrWkTX/7W9/q3/5l3/Rjh07QjBD+3RkzQ8ePKjNmzdr6tSpKikp0R//+Ed973vfU0NDgwoKCkIx7YjWkTW/7777dPLkSX3ta1+TMUYXLlzQww8/rIULF4Ziyt3SpV5Ha2pqdO7cOfXo0aNd98OZHVht2bJlWrt2rd566y3Fx8eHezpWOnPmjPLy8vTKK6+ob9++4Z5Ot9HU1KTExES9/PLLGjNmjCZPnqxFixZp1apV4Z6atbZs2aKlS5fqpZdeUmVlpTZs2KB3331XTz75ZLinhivgzE479e3bV9HR0aqqqmq1vaqqSh6Pp819PB5PQONxsY6se4unn35ay5Yt03vvvaebbropmNO0SqBrfuDAAR0+fFh33nmnf1tTU5MkKSYmRvv379eQIUOCO+kI15HneXJysmJjYxUdHe3fNnz4cHm9XtXX1ysuLi6oc450HVnzH/zgB8rLy9ODDz4oSRo5cqTOnj2rmTNnatGiRYqK4vxBZ7vU66jL5Wr3WR2JMzvtFhcXpzFjxqisrMy/rampSWVlZcrKympzn6ysrFbjJWnTpk2XHI+LdWTdJempp57Sk08+qdLSUqWnp4diqtYIdM2HDRumnTt3aseOHf7bt771Ld16663asWOHUlNTQzn9iNSR5/ktt9yiP/7xj/6wlKT/+Z//UXJyMqHTDh1Z89ra2ouCpiU2DX9mMig67XU0sGunu7e1a9cap9NpiouLzZ49e8zMmTNNQkKC8Xq9xhhj8vLyzPz58/3jP/zwQxMTE2Oefvpps3fvXlNQUMBHzzsg0HVftmyZiYuLM2+++ab5/PPP/bczZ86E6xAiTqBr/mV8Gitwga75kSNHTK9evczs2bPN/v37zcaNG01iYqL58Y9/HK5DiDiBrnlBQYHp1auX+bd/+zdz8OBB81//9V9myJAh5t577w3XIUScM2fOmO3bt5vt27cbSebZZ58127dvN59++qkxxpj58+ebvLw8//iWj54/9thjZu/evWbFihV89DwUXnjhBTNgwAATFxdnMjIyzO9+9zv/18aPH2+mT5/eavwvfvELc/3115u4uDhz4403mnfffTfEM7ZDIOv+la98xUi66FZQUBD6iUewQJ/rf4nY6ZhA13zr1q0mMzPTOJ1OM3jwYPOTn/zEXLhwIcSzjmyBrHlDQ4P54Q9/aIYMGWLi4+NNamqq+d73vmf+9Kc/hX7iEerXv/51m/9/blnn6dOnm/Hjx1+0T1pamomLizODBw82//qv/xrw4zqM4dwbAACwF9fsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAyDinThxQh6PR0uXLvVv27p1q+Li4lRWVhbGmQHoCvhDoACsUFJSotzcXG3dulVDhw5VWlqa7rrrLj377LPhnhqAMCN2AFhj1qxZeu+995Senq6dO3dq27Ztcjqd4Z4WgDAjdgBY49y5cxoxYoSOHj2qiooKjRw5MtxTAtAFcM0OAGscOHBAx48fV1NTkw4fPhzu6QDoIjizA8AK9fX1ysjIUFpamoYOHaqioiLt3LlTiYmJ4Z4agDAjdgBY4bHHHtObb76p3//+97r66qs1fvx4ud1ubdy4MdxTAxBmvI0FIOJt2bJFRUVFev311+VyuRQVFaXXX39dH3zwgVauXBnu6QEIM87sAAAAq3FmBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNX+P1V4MiVWEqqRAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -331,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "3bb4dc9b", "metadata": {}, "outputs": [ @@ -342,14 +351,16 @@ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" + "HPU available: False, using: 0 HPUs\n", + "/Users/alessio/opt/anaconda3/envs/pina/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "Missing logger folder: /Users/alessio/Downloads/lightning_logs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1499: : 1it [00:00, 272.55it/s, v_num=3, x0_loss=7.71e-6, D_loss=0.000734, mean_loss=0.000371]" + "Epoch 1499: | | 1/? [00:00<00:00, 167.08it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401]" ] }, { @@ -363,7 +374,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1499: : 1it [00:00, 167.14it/s, v_num=3, x0_loss=7.71e-6, D_loss=0.000734, mean_loss=0.000371]\n" + "Epoch 1499: | | 1/? [00:00<00:00, 102.49it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401]\n" ] } ], @@ -402,19 +413,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "f5fbf362", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'x0_loss': tensor(7.7149e-06),\n", - " 'D_loss': tensor(0.0007),\n", + "{'x0_loss': tensor(1.0674e-05),\n", + " 'D_loss': tensor(0.0008),\n", " 'mean_loss': tensor(0.0004)}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -434,13 +445,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "19078eb5", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -473,13 +491,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "bf6211e6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -507,16 +525,22 @@ "source": [ "## What's next?\n", "\n", - "Nice you have completed the introductory tutorial of **PINA**! There are multiple directions you can go now:\n", + "Congratulations on completing the introductory tutorial of **PINA**! There are several directions you can go now:\n", "\n", "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", "\n", "2. Train the network using other types of models (see `pina.model`)\n", "\n", - "3. GPU trainining and benchmark the speed\n", + "3. GPU training and speed benchmarking\n", "\n", "4. Many more..." ] + }, + { + "cell_type": "markdown", + "id": "2931091d", + "metadata": {}, + "source": [] } ], "metadata": { @@ -538,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py index b6919d3..ab257e0 100644 --- a/tutorials/tutorial1/tutorial.py +++ b/tutorials/tutorial1/tutorial.py @@ -11,10 +11,10 @@ # # Specifically, the tutorial aims to introduce the following topics: # -# * Explaining how to build **PINA** Problem, -# * Showing how to generate data for `PINN` straining +# * Explaining how to build **PINA** Problems, +# * Showing how to generate data for `PINN` training # -# These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem busing the `PINN` solver. +# These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem using the `PINN` solver. # ## Build a PINA problem @@ -47,7 +47,7 @@ # # Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$), this is done because in **PINA** the `torch.Tensor`s are labelled, allowing the user maximal flexibility for the manipulation of the tensor. The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\in[0,1]$. # -# What about if our equation is also time dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`: +# What if our equation is also time-dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`: # # In[1]: @@ -122,16 +122,16 @@ class SimpleODE(SpatialProblem): problem = SimpleODE() -# After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations, and internal checks are done inside **PINA**. +# After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations and internal checks are done inside **PINA**. # # Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference). # -# Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but is not mandatory for problem definition. +# Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but it is not mandatory for problem definition. # # ## Generate data # -# Data for training can come in form of direct numerical simulation reusults, or points in the domains. In case we do unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class. +# Data for training can come in form of direct numerical simulation results, or points in the domains. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class. # In[3]: @@ -139,7 +139,7 @@ problem = SimpleODE() # sampling 20 points in [0, 1] through discretization in all locations problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') -# sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0 +# sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0 problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) @@ -168,7 +168,7 @@ print('Input points labels:', problem.input_pts['D'].labels) # To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class -# In[6]: +# In[5]: from pina import Plotter @@ -181,7 +181,7 @@ pl.plot_samples(problem=problem) # Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solvers`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`. -# In[7]: +# In[6]: from pina import Trainer @@ -210,7 +210,7 @@ trainer.train() # After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics` -# In[8]: +# In[7]: # inspecting final loss @@ -219,7 +219,7 @@ trainer.logged_metrics # By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. -# In[9]: +# In[8]: # plotting the solution @@ -228,7 +228,7 @@ pl.plot(solver=pinn) # The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss: -# In[10]: +# In[9]: pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True) @@ -238,12 +238,14 @@ pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True) # ## What's next? # -# Nice you have completed the introductory tutorial of **PINA**! There are multiple directions you can go now: +# Congratulations on completing the introductory tutorial of **PINA**! There are several directions you can go now: # # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy # # 2. Train the network using other types of models (see `pina.model`) # -# 3. GPU trainining and benchmark the speed +# 3. GPU training and speed benchmarking # # 4. Many more... + +# diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb index 871b822..32e74bc 100644 --- a/tutorials/tutorial2/tutorial.ipynb +++ b/tutorials/tutorial2/tutorial.ipynb @@ -116,7 +116,7 @@ "source": [ "After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals.\n", "\n", - "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-7}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training." + "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training." ] }, { @@ -561,7 +561,7 @@ "source": [ "## What's next?\n", "\n", - "Nice you have completed the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now:\n", + "Congratulations on completing the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now:\n", "\n", "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", "\n", diff --git a/tutorials/tutorial2/tutorial.py b/tutorials/tutorial2/tutorial.py index b181ed6..afc2410 100644 --- a/tutorials/tutorial2/tutorial.py +++ b/tutorials/tutorial2/tutorial.py @@ -80,7 +80,7 @@ problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', ' # After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals. # -# In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-7}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training. +# In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006 and $l_2$ weight regularization set to $10^{-8}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training. # In[3]: @@ -252,7 +252,7 @@ plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features') # ## What's next? # -# Nice you have completed the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now: +# Congratulations on completing the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now: # # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy # diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index 0a2688d..5148bbb 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -54,7 +54,7 @@ "\\end{cases}\n", "\\end{equation}\n", "\n", - "where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one." + "where $D$ is a squared domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one." ] }, { @@ -305,7 +305,7 @@ "id": "35e51649", "metadata": {}, "source": [ - "The results are not so great, and we can clearly see that as time progress the solution get worse.... Can we do better?\n", + "The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better?\n", "\n", "A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n", "\n", @@ -491,7 +491,7 @@ "id": "b7338109", "metadata": {}, "source": [ - "We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when the time evolved. By imposing the initial condition the network is able to correctly solve the problem." + "We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem." ] }, { @@ -501,7 +501,7 @@ "source": [ "## What's next?\n", "\n", - "Nice you have completed the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:\n", + "Congratulations on completing the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:\n", "\n", "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", "\n", diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py index b2ff8e4..5933b2e 100644 --- a/tutorials/tutorial3/tutorial.py +++ b/tutorials/tutorial3/tutorial.py @@ -34,7 +34,7 @@ from pina import Condition, Plotter # \end{cases} # \end{equation} # -# where $D$ is a square domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one. +# where $D$ is a squared domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one. # Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one. @@ -142,7 +142,7 @@ print('Plotting at t=1') plotter.plot(pinn, fixed_variables={'t': 1.0}) -# The results are not so great, and we can clearly see that as time progress the solution get worse.... Can we do better? +# The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better? # # A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as: # @@ -207,11 +207,11 @@ print('Plotting at t=1') plotter.plot(pinn, fixed_variables={'t': 1.0}) -# We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when the time evolved. By imposing the initial condition the network is able to correctly solve the problem. +# We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem. # ## What's next? # -# Nice you have completed the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now: +# Congratulations on completing the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now: # # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy # diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb index bc36921..57c6786 100644 --- a/tutorials/tutorial4/tutorial.ipynb +++ b/tutorials/tutorial4/tutorial.ipynb @@ -105,7 +105,7 @@ "f(x, y) = [\\sin(\\pi x) \\sin(\\pi y), -\\sin(\\pi x) \\sin(\\pi y)] \\quad (x,y)\\in[0,1]\\times[0,1]\n", "$$\n", "\n", - "using a batch size of one." + "using a batch size equal to 1." ] }, { @@ -130,14 +130,14 @@ "# points in the mesh fixed to 200\n", "N = 200\n", "\n", - "# vectorial 2 dimensional function, number_input_fileds=2\n", - "number_input_fileds = 2\n", + "# vectorial 2 dimensional function, number_input_fields=2\n", + "number_input_fields = 2\n", "\n", "# 2 dimensional spatial variables, D = 2 + 1 = 3\n", "D = 3\n", "\n", "# create the function f domain as random 2d points in [0, 1]\n", - "domain = torch.rand(size=(batch_size, number_input_fileds, N, D-1))\n", + "domain = torch.rand(size=(batch_size, number_input_fields, N, D-1))\n", "print(f\"Domain has shape: {domain.shape}\")\n", "\n", "# create the functions\n", @@ -146,7 +146,7 @@ "f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1])\n", "\n", "# stacking the input domain and field values\n", - "data = torch.empty(size=(batch_size, number_input_fileds, N, D))\n", + "data = torch.empty(size=(batch_size, number_input_fields, N, D))\n", "data[..., :-1] = domain # copy the domain\n", "data[:, 0, :, -1] = f1 # copy first field value\n", "data[:, 1, :, -1] = f1 # copy second field value\n", @@ -174,7 +174,7 @@ "1. `domain`: square domain (the only implemented) $[0,1]\\times[0,5]$. The minimum value is always zero, while the maximum is specified by the user\n", "2. `start`: start position of the filter, coordinate $(0, 0)$\n", "3. `jump`: the jumps of the centroid of the filter to the next position $(0.1, 0.3)$\n", - "4. `direction`: the directions of the jump, with `1 = right`, `0 = no jump`,`-1 = left` with respect to the current position\n", + "4. `direction`: the directions of the jump, with `1 = right`, `0 = no jump`, `-1 = left` with respect to the current position\n", "\n", "**Note**\n", "\n", @@ -188,9 +188,9 @@ "source": [ "### Filter definition\n", "\n", - "Having defined all the previous blocks we are able to construct the continuous filter.\n", + "Having defined all the previous blocks, we are now able to construct the continuous filter.\n", "\n", - "Suppose we would like to get an ouput with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$." + "Suppose we would like to get an output with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$." ] }, { @@ -220,7 +220,7 @@ " }\n", "\n", "# creating the filter \n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n", + "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", " output_numb_field=1,\n", " filter_dim=filter_dim,\n", " stride=stride)" @@ -242,7 +242,7 @@ "outputs": [], "source": [ "# creating the filter + optimization\n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n", + "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", " output_numb_field=1,\n", " filter_dim=filter_dim,\n", " stride=stride,\n", @@ -254,7 +254,7 @@ "id": "f99c290e", "metadata": {}, "source": [ - "Let's try to do a forward pass" + "Let's try to do a forward pass:" ] }, { @@ -310,7 +310,7 @@ " return self.model(x)\n", "\n", "\n", - "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n", + "cConv = ContinuousConvBlock(input_numb_field=number_input_fields,\n", " output_numb_field=1,\n", " filter_dim=filter_dim,\n", " stride=stride,\n", @@ -380,7 +380,7 @@ "id": "7f076010", "metadata": {}, "source": [ - "Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\\times[0,27]$ domain, and the pixels value are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:" + "Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\\times[0,27]$ domain, and the pixels values are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:" ] }, { @@ -478,7 +478,7 @@ "id": "4374c15c", "metadata": {}, "source": [ - "Let's try to train it using a simple pytorch training loop. We train for juts 1 epoch using Adam optimizer with a $0.001$ learning rate." + "Let's try to train it using a simple pytorch training loop. We train for just 1 epoch using Adam optimizer with a $0.001$ learning rate." ] }, { @@ -556,7 +556,7 @@ "id": "47fa3d0e", "metadata": {}, "source": [ - "Let's see the performance on the train set!" + "Let's see the performance on the test set!" ] }, { @@ -595,7 +595,7 @@ "id": "25cf2878", "metadata": {}, "source": [ - "As we can see we have very good performance for having traing only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now." + "As we can see we have very good performance for having trained only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now." ] }, { @@ -876,7 +876,7 @@ "id": "206141f9", "metadata": {}, "source": [ - "As we can see the two are really similar! We can compute the $l_2$ error quite easily as well:" + "As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well:" ] }, { @@ -916,7 +916,7 @@ "source": [ "### Filter for upsampling\n", "\n", - "Suppose we have already the hidden dimension and we want to upsample on a differen grid with more points. Let's see how to do it:" + "Suppose we have already the hidden representation and we want to upsample on a differen grid with more points. Let's see how to do it:" ] }, { @@ -946,7 +946,7 @@ "input_data2[0, 0, :, -1] = torch.sin(pi *\n", " grid2[:, 0]) * torch.sin(pi * grid2[:, 1])\n", "\n", - "# get the hidden dimension representation from original input\n", + "# get the hidden representation from original input\n", "latent = net.encoder(input_data)\n", "\n", "# upsample on the second input_data2\n", @@ -996,13 +996,13 @@ "id": "465cbd16", "metadata": {}, "source": [ - "### Autoencoding at different resolution\n", - "In the previous example we already had the hidden dimension (of original input) and we used it to upsample. Sometimes however we have a more fine mesh solution and we simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:" + "### Autoencoding at different resolutions\n", + "In the previous example we already had the hidden representation (of the original input) and we used it to upsample. Sometimes however we could have a finer mesh solution and we would simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "75ed28f5", "metadata": {}, "outputs": [ @@ -1034,7 +1034,7 @@ "input_data2[0, 0, :, -1] = torch.sin(pi *\n", " grid2[:, 0]) * torch.sin(pi * grid2[:, 1])\n", "\n", - "# get the hidden dimension representation from more fine mesh input\n", + "# get the hidden representation from finer mesh input\n", "latent = net.encoder(input_data2)\n", "\n", "# upsample on the second input_data2\n", diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py index bc5556c..f5ca37b 100644 --- a/tutorials/tutorial4/tutorial.py +++ b/tutorials/tutorial4/tutorial.py @@ -1,20 +1,21 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 4: continuous convolutional filter +# # Tutorial: Unstructured convolutional autoencoder via continuous convolution -# In this tutorial, we will show how to use the Continuous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work [**A Continuous Convolutional Trainable Filter for Modelling Unstructured Data**](https://arxiv.org/abs/2210.13416). +# In this tutorial, we will show how to use the Continuous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work [*A Continuous Convolutional Trainable Filter for Modelling Unstructured Data*](https://arxiv.org/abs/2210.13416). -# First of all we import the modules needed for the tutorial, which include: -# -# * `ContinuousConv` class from `pina.model.layers` which implements the continuous convolutional filter -# * `PyTorch` and `Matplotlib` for tensorial operations and visualization respectively +# First of all we import the modules needed for the tutorial: # In[1]: import torch import matplotlib.pyplot as plt +from pina.problem import AbstractProblem +from pina.solvers import SupervisedSolver +from pina.trainer import Trainer +from pina import Condition, LabelTensor from pina.model.layers import ContinuousConvBlock import torchvision # for MNIST dataset from pina.model import FeedForward # for building AE and MNIST classification @@ -54,7 +55,7 @@ from pina.model import FeedForward # for building AE and MNIST classification # f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1] # $$ # -# using a batch size of one. +# using a batch size equal to 1. # In[2]: @@ -65,14 +66,14 @@ batch_size = 1 # points in the mesh fixed to 200 N = 200 -# vectorial 2 dimensional function, number_input_fileds=2 -number_input_fileds = 2 +# vectorial 2 dimensional function, number_input_fields=2 +number_input_fields = 2 # 2 dimensional spatial variables, D = 2 + 1 = 3 D = 3 # create the function f domain as random 2d points in [0, 1] -domain = torch.rand(size=(batch_size, number_input_fileds, N, D-1)) +domain = torch.rand(size=(batch_size, number_input_fields, N, D-1)) print(f"Domain has shape: {domain.shape}") # create the functions @@ -81,7 +82,7 @@ f1 = torch.sin(pi * domain[:, 0, :, 0]) * torch.sin(pi * domain[:, 0, :, 1]) f2 = - torch.sin(pi * domain[:, 1, :, 0]) * torch.sin(pi * domain[:, 1, :, 1]) # stacking the input domain and field values -data = torch.empty(size=(batch_size, number_input_fileds, N, D)) +data = torch.empty(size=(batch_size, number_input_fields, N, D)) data[..., :-1] = domain # copy the domain data[:, 0, :, -1] = f1 # copy first field value data[:, 1, :, -1] = f1 # copy second field value @@ -104,7 +105,7 @@ print(f"Filter input data has shape: {data.shape}") # 1. `domain`: square domain (the only implemented) $[0,1]\times[0,5]$. The minimum value is always zero, while the maximum is specified by the user # 2. `start`: start position of the filter, coordinate $(0, 0)$ # 3. `jump`: the jumps of the centroid of the filter to the next position $(0.1, 0.3)$ -# 4. `direction`: the directions of the jump, with `1 = right`, `0 = no jump`,`-1 = left` with respect to the current position +# 4. `direction`: the directions of the jump, with `1 = right`, `0 = no jump`, `-1 = left` with respect to the current position # # **Note** # @@ -112,9 +113,9 @@ print(f"Filter input data has shape: {data.shape}") # ### Filter definition # -# Having defined all the previous blocks we are able to construct the continuous filter. +# Having defined all the previous blocks, we are now able to construct the continuous filter. # -# Suppose we would like to get an ouput with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$. +# Suppose we would like to get an output with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$. # In[3]: @@ -130,7 +131,7 @@ stride = {"domain": [1, 1], } # creating the filter -cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, +cConv = ContinuousConvBlock(input_numb_field=number_input_fields, output_numb_field=1, filter_dim=filter_dim, stride=stride) @@ -142,14 +143,14 @@ cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, # creating the filter + optimization -cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, +cConv = ContinuousConvBlock(input_numb_field=number_input_fields, output_numb_field=1, filter_dim=filter_dim, stride=stride, optimize=True) -# Let's try to do a forward pass +# Let's try to do a forward pass: # In[5]: @@ -182,7 +183,7 @@ class SimpleKernel(torch.nn.Module): return self.model(x) -cConv = ContinuousConvBlock(input_numb_field=number_input_fileds, +cConv = ContinuousConvBlock(input_numb_field=number_input_fields, output_numb_field=1, filter_dim=filter_dim, stride=stride, @@ -231,7 +232,7 @@ test_loader = DataLoader(train_data, batch_size=batch_size, sampler=SubsetRandomSampler(subsample_train_indices)) -# Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels value are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter: +# Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels values are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter: # In[8]: @@ -300,7 +301,7 @@ class ContinuousClassifier(torch.nn.Module): net = ContinuousClassifier() -# Let's try to train it using a simple pytorch training loop. We train for juts 1 epoch using Adam optimizer with a $0.001$ learning rate. +# Let's try to train it using a simple pytorch training loop. We train for just 1 epoch using Adam optimizer with a $0.001$ learning rate. # In[10]: @@ -336,7 +337,7 @@ for epoch in range(1): # loop over the dataset multiple times running_loss = 0.0 -# Let's see the performance on the train set! +# Let's see the performance on the test set! # In[11]: @@ -357,7 +358,7 @@ print( f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}') -# As we can see we have very good performance for having traing only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now. +# As we can see we have very good performance for having trained only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now. # ## Building a Continuous Convolutional Autoencoder # @@ -463,7 +464,7 @@ class Decoder(torch.nn.Module): # Very good! Notice that in the `Decoder` class in the `forward` pass we have used the `.transpose()` method of the `ContinuousConvolution` class. This method accepts the `weights` for upsampling and the `grid` on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the `hidden_dimension` variable. We apply the sigmoid on the output since the field value is between $[0, 1]$. -# In[14]: +# In[17]: class Autoencoder(torch.nn.Module): @@ -482,42 +483,32 @@ class Autoencoder(torch.nn.Module): out = self.decoder(weights, grid) return out - net = Autoencoder() -# Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. +# Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. We use the `SupervisedSolver` as solver, and the problem is a simple problem created by inheriting from `AbstractProblem`. It takes approximately two minutes to train on CPU. -# In[15]: +# In[19]: -# setting the seed -torch.manual_seed(seed) +# define the problem +class CircleProblem(AbstractProblem): + input_variables = ['x', 'y', 'f'] + output_variables = input_variables + conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))} -# optimizer and loss function -optimizer = torch.optim.Adam(net.parameters(), lr=0.001) -criterion = torch.nn.MSELoss() -max_epochs = 150 +# define the solver +solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) -for epoch in range(max_epochs): # loop over the dataset multiple times - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = net(input_data) - loss = criterion(outputs[..., -1], input_data[..., -1]) - loss.backward() - optimizer.step() - - # print statistics - if epoch % 10 ==9: - print(f'epoch [{epoch + 1}/{max_epochs}] loss [{loss.item():.2}]') +# train +trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer.train() + # Let's visualize the two solutions side by side! -# In[16]: +# In[20]: net.eval() @@ -538,9 +529,9 @@ plt.tight_layout() plt.show() -# As we can see the two are really similar! We can compute the $l_2$ error quite easily as well: +# As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well: -# In[17]: +# In[21]: def l2_error(input_, target): @@ -554,9 +545,9 @@ print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}') # ### Filter for upsampling # -# Suppose we have already the hidden dimension and we want to upsample on a differen grid with more points. Let's see how to do it: +# Suppose we have already the hidden representation and we want to upsample on a differen grid with more points. Let's see how to do it: -# In[18]: +# In[22]: # setting the seed @@ -568,7 +559,7 @@ input_data2[0, 0, :, :-1] = grid2 input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) -# get the hidden dimension representation from original input +# get the hidden representation from original input latent = net.encoder(input_data) # upsample on the second input_data2 @@ -589,16 +580,16 @@ plt.show() # As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now: -# In[19]: +# In[23]: print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') -# ### Autoencoding at different resolution -# In the previous example we already had the hidden dimension (of original input) and we used it to upsample. Sometimes however we have a more fine mesh solution and we simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this: +# ### Autoencoding at different resolutions +# In the previous example we already had the hidden representation (of the original input) and we used it to upsample. Sometimes however we could have a finer mesh solution and we would simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this: -# In[20]: +# In[ ]: # setting the seed @@ -610,7 +601,7 @@ input_data2[0, 0, :, :-1] = grid2 input_data2[0, 0, :, -1] = torch.sin(pi * grid2[:, 0]) * torch.sin(pi * grid2[:, 1]) -# get the hidden dimension representation from more fine mesh input +# get the hidden representation from finer mesh input latent = net.encoder(input_data2) # upsample on the second input_data2 @@ -635,4 +626,10 @@ print( # ## What's next? # -# We have shown the basic usage of a convolutional filter. In the next tutorials we will show how to combine the PINA framework with the convolutional filter to train in few lines and efficiently a Neural Network! +# We have shown the basic usage of a convolutional filter. There are additional extensions possible: +# +# 1. Train using Physics Informed strategies +# +# 2. Use the filter to build an unstructured convolutional autoencoder for reduced order modelling +# +# 3. Many more... diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb index aa96cbf..3717132 100644 --- a/tutorials/tutorial5/tutorial.ipynb +++ b/tutorials/tutorial5/tutorial.ipynb @@ -14,7 +14,7 @@ "metadata": {}, "source": [ "In this tutorial we are going to solve the Darcy flow problem in two dimensions, presented in [*Fourier Neural Operator for\n", - "Parametric Partial Differential Equation*](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input output operations." + "Parametric Partial Differential Equation*](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input-output operations." ] }, { @@ -42,7 +42,7 @@ "source": [ "## Data Generation\n", "\n", - "We will focus on solving the a specfic PDE, the **Darcy Flow** equation. The Darcy PDE is a second order, elliptic PDE with the following form:\n", + "We will focus on solving a specific PDE, the **Darcy Flow** equation. The Darcy PDE is a second-order elliptic PDE with the following form:\n", "\n", "$$\n", "-\\nabla\\cdot(k(x, y)\\nabla u(x, y)) = f(x) \\quad (x, y) \\in D.\n", @@ -233,7 +233,7 @@ "id": "6b5e5aa6", "metadata": {}, "source": [ - "## Solving the problem with a Fuorier Neural Operator (FNO)\n", + "## Solving the problem with a Fourier Neural Operator (FNO)\n", "\n", "We will now move to solve the problem using a FNO. Since we are learning operator this approach is better suited, as we shall see." ] diff --git a/tutorials/tutorial5/tutorial.py b/tutorials/tutorial5/tutorial.py index 7414a85..3b28a9f 100644 --- a/tutorials/tutorial5/tutorial.py +++ b/tutorials/tutorial5/tutorial.py @@ -4,7 +4,7 @@ # # Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator # In this tutorial we are going to solve the Darcy flow problem in two dimensions, presented in [*Fourier Neural Operator for -# Parametric Partial Differential Equation*](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input output operations. +# Parametric Partial Differential Equation*](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input-output operations. # In[1]: @@ -22,7 +22,7 @@ import matplotlib.pyplot as plt # ## Data Generation # -# We will focus on solving the a specfic PDE, the **Darcy Flow** equation. The Darcy PDE is a second order, elliptic PDE with the following form: +# We will focus on solving a specific PDE, the **Darcy Flow** equation. The Darcy PDE is a second-order elliptic PDE with the following form: # # $$ # -\nabla\cdot(k(x, y)\nabla u(x, y)) = f(x) \quad (x, y) \in D. @@ -112,7 +112,7 @@ err = float(metric_err(u_test.squeeze(-1), solver.neural_net(k_test).squeeze(-1) print(f'Final error testing {err:.2f}%') -# ## Solving the problem with a Fuorier Neural Operator (FNO) +# ## Solving the problem with a Fourier Neural Operator (FNO) # # We will now move to solve the problem using a FNO. Since we are learning operator this approach is better suited, as we shall see. diff --git a/tutorials/tutorial6/tutorial.ipynb b/tutorials/tutorial6/tutorial.ipynb index 198405a..6ef13e9 100644 --- a/tutorials/tutorial6/tutorial.ipynb +++ b/tutorials/tutorial6/tutorial.ipynb @@ -44,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional, but it's trivial the extension to 3D (and higher) cases. The geometries allows also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without." + "We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional case, but the extension to 3D (and higher) cases is trivial. The geometries allow also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without." ] }, { @@ -65,7 +65,7 @@ "source": [ "The `{'x': [0, 2], 'y': [0, 2]}` are the bounds of the `CartesianDomain` being created. \n", "\n", - "To visualize these shapes, we need to sample points on them. We will use the `sample` method of the `CartesianDomain` and `EllipsoidDomain` classes. This method takes a `n` argument which is the number of points to sample. It also takes different modes to sample such as random." + "To visualize these shapes, we need to sample points on them. We will use the `sample` method of the `CartesianDomain` and `EllipsoidDomain` classes. This method takes a `n` argument which is the number of points to sample. It also takes different modes to sample, such as `'random'`." ] }, { @@ -84,7 +84,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see the samples of each of the geometries to see what we are working with." + "We can see the samples of each geometry to see what we are working with." ] }, { @@ -255,7 +255,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can of course sample points over the new geometries, by using the `sample` method as before. We highlihgt that the available sample strategy here is only *random*." + "We can of course sample points over the new geometries, by using the `sample` method as before. We highlight that the available sample strategy here is only *random*." ] }, { @@ -395,7 +395,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Because the `Location` class we are inherting from requires both a `sample` method and `is_inside` method, we will create them and just add in \"pass\" for the moment." + "Because the `Location` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in \"pass\" for the moment." ] }, { diff --git a/tutorials/tutorial6/tutorial.py b/tutorials/tutorial6/tutorial.py index a1e40a8..966609d 100644 --- a/tutorials/tutorial6/tutorial.py +++ b/tutorials/tutorial6/tutorial.py @@ -25,7 +25,7 @@ def plot_scatter(ax, pts, title): # ## Built-in Geometries -# We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional, but it's trivial the extension to 3D (and higher) cases. The geometries allows also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without. +# We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional case, but the extension to 3D (and higher) cases is trivial. The geometries allow also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without. # In[2]: @@ -37,7 +37,7 @@ ellipsoid_border = EllipsoidDomain({'x': [2, 4], 'y': [2, 4]}, sample_surface=Tr # The `{'x': [0, 2], 'y': [0, 2]}` are the bounds of the `CartesianDomain` being created. # -# To visualize these shapes, we need to sample points on them. We will use the `sample` method of the `CartesianDomain` and `EllipsoidDomain` classes. This method takes a `n` argument which is the number of points to sample. It also takes different modes to sample such as random. +# To visualize these shapes, we need to sample points on them. We will use the `sample` method of the `CartesianDomain` and `EllipsoidDomain` classes. This method takes a `n` argument which is the number of points to sample. It also takes different modes to sample, such as `'random'`. # In[3]: @@ -47,7 +47,7 @@ ellipsoid_no_border_samples = ellipsoid_no_border.sample(n=1000, mode='random') ellipsoid_border_samples = ellipsoid_border.sample(n=1000, mode='random') -# We can see the samples of each of the geometries to see what we are working with. +# We can see the samples of each geometry to see what we are working with. # In[4]: @@ -118,7 +118,7 @@ cart_ellipse_b_union = Union([cartesian, ellipsoid_border]) three_domain_union = Union([cartesian, ellipsoid_no_border, ellipsoid_border]) -# We can of course sample points over the new geometries, by using the `sample` method as before. We highlihgt that the available sample strategy here is only *random*. +# We can of course sample points over the new geometries, by using the `sample` method as before. We highlight that the available sample strategy here is only *random*. # In[8]: @@ -180,7 +180,7 @@ class Heart(Location): -# Because the `Location` class we are inherting from requires both a `sample` method and `is_inside` method, we will create them and just add in "pass" for the moment. +# Because the `Location` class we are inheriting from requires both a `sample` method and `is_inside` method, we will create them and just add in "pass" for the moment. # In[13]: diff --git a/tutorials/tutorial7/tutorial.ipynb b/tutorials/tutorial7/tutorial.ipynb index d27ec2f..54b13a2 100644 --- a/tutorials/tutorial7/tutorial.ipynb +++ b/tutorials/tutorial7/tutorial.ipynb @@ -35,7 +35,7 @@ "- find the solution $u$ that satisfies the Poisson equation;\n", "- find the unknown parameters ($\\mu_1$, $\\mu_2$) that better fit some given data (third equation in the system above).\n", "\n", - "In order to achieve both the goals we will need to define an `InverseProblem` in PINA." + "In order to achieve both goals we will need to define an `InverseProblem` in PINA." ] }, { @@ -100,7 +100,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -134,7 +134,7 @@ "id": "c46410fa-2718-4fc9-977a-583fe2390028", "metadata": {}, "source": [ - "Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\\mu_1$, $\\mu_2$) belong. Notice that the laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process." + "Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\\mu_1$, $\\mu_2$) belong. Notice that the Laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process." ] }, { @@ -198,7 +198,7 @@ "id": "6b264569-57b3-458d-bb69-8e94fe89017d", "metadata": {}, "source": [ - "Then, we define the model of the neural network we want to use. Here we used a model which impose hard constrains on the boundary conditions, as also done in the Wave tutorial!" + "Then, we define the neural network model we want to use. Here we used a model which imposes hard constrains on the boundary conditions, as also done in the Wave tutorial!" ] }, { @@ -304,7 +304,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -334,14 +334,6 @@ "plt.ylabel('Parameter value')\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6a0ba58", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/tutorials/tutorial7/tutorial.py b/tutorials/tutorial7/tutorial.py index 48fc953..9905ac8 100644 --- a/tutorials/tutorial7/tutorial.py +++ b/tutorials/tutorial7/tutorial.py @@ -14,12 +14,12 @@ # \end{cases} # \end{equation} # where $\Omega$ is a square domain $[-2, 2] \times [-2, 2]$, and $\partial \Omega=\Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4$ is the union of the boundaries of the domain. -# +# # This kind of problem, namely the "inverse problem", has two main goals: # - find the solution $u$ that satisfies the Poisson equation; # - find the unknown parameters ($\mu_1$, $\mu_2$) that better fit some given data (third equation in the system above). -# -# In order to achieve both the goals we will need to define an `InverseProblem` in PINA. +# +# In order to achieve both goals we will need to define an `InverseProblem` in PINA. # Let's start with useful imports. @@ -63,7 +63,7 @@ plt.show() # ### Inverse problem definition in PINA -# Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\mu_1$, $\mu_2$) belong. Notice that the laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process. +# Then, we initialize the Poisson problem, that is inherited from the `SpatialProblem` and from the `InverseProblem` classes. We here have to define all the variables, and the domain where our unknown parameters ($\mu_1$, $\mu_2$) belong. Notice that the Laplace equation takes as inputs also the unknown variables, that will be treated as parameters that the neural network optimizes during the training process. # In[4]: @@ -117,7 +117,7 @@ class Poisson(SpatialProblem, InverseProblem): problem = Poisson() -# Then, we define the model of the neural network we want to use. Here we used a model which impose hard constrains on the boundary conditions, as also done in the Wave tutorial! +# Then, we define the neural network model we want to use. Here we used a model which imposes hard constrains on the boundary conditions, as also done in the Wave tutorial! # In[5]: @@ -160,7 +160,7 @@ class SaveParameters(Callback): # Then, we define the `PINN` object and train the solver using the `Trainer`. -# In[8]: +# In[ ]: ### train the problem with PINN @@ -181,7 +181,7 @@ epochs_saved = range(99, max_epochs, 100) parameters = torch.empty((int(max_epochs/100), 2)) for i, epoch in enumerate(epochs_saved): params_torch = torch.load('{}/parameters_epoch{}'.format(tmp_dir, epoch)) - for e, var in enumerate(pinn.problem.unknown_variables): + for e, var in enumerate(pinn.problem.unknown_variables): parameters[i, e] = params_torch[var].data # Plot parameters diff --git a/tutorials/tutorial8/tutorial.ipynb b/tutorials/tutorial8/tutorial.ipynb index 6a11689..9a37448 100644 --- a/tutorials/tutorial8/tutorial.ipynb +++ b/tutorials/tutorial8/tutorial.ipynb @@ -15,7 +15,7 @@ "source": [ "The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists of predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion.\n", "\n", - "In particular we are going to use the Proper Orthogonal Decomposition with Neural Network (PODNN) [2], which basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a NN. In this example, we use a simple multilayer perceptron, but the plenty of different archiutectures can be plugged as well.\n", + "In particular we are going to use the Proper Orthogonal Decomposition with Neural Network (PODNN) [2], which basically performs a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a NN. In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well.\n", "\n", "#### References\n", "1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. \n", @@ -118,7 +118,7 @@ "id": "bef4d79d", "metadata": {}, "source": [ - "The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict for any new test parameter the solution.\n", + "The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter.\n", "To properly validate the accuracy, we initially split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%). It must be said that, to plug the snapshots into **PINA**, we have to cast them to `LabelTensor` objects." ] }, @@ -172,7 +172,7 @@ "id": "6b264569-57b3-458d-bb69-8e94fe89017d", "metadata": {}, "source": [ - "Then, we define the model we want to use: basically we have a MLP architecture that takes in input the parameter and return the *modal coefficients*, so the reduced dimension representation (the coordinates in the POD space). Such latent variable is the projected to the original space using the POD modes, which are computed and stored in the `PODBlock` object." + "Then, we define the model we want to use: an MLP architecture which takes in input the parameter and returns the *modal coefficients*, i.e.the interpolated coefficients of the POD expansion. Such coefficients are projected to the original space using the POD modes, which are computed and stored in the `PODBlock` object." ] }, { @@ -227,7 +227,7 @@ "id": "16e1f085-7818-4624-92a1-bf7010dbe528", "metadata": {}, "source": [ - "We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the back-propagation approach. Only the weights of the MLP are actually trained during the optimization loop." + "We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop." ] }, { @@ -254,7 +254,7 @@ "id": "aab51202-36a7-40d2-b96d-47af8892cd2c", "metadata": {}, "source": [ - "Now that we set the `Problem` and the `Model`, we have just to train the model and use it for predict the test snapshots." + "Now that we have set the `Problem` and the `Model`, we have just to train the model and use it for predicting the test snapshots." ] }, { @@ -320,7 +320,7 @@ "id": "3234710e", "metadata": {}, "source": [ - "Done! Now the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one." + "Done! Now that the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one." ] }, { diff --git a/tutorials/tutorial9/tutorial.ipynb b/tutorials/tutorial9/tutorial.ipynb index 6299e84..35228a9 100644 --- a/tutorials/tutorial9/tutorial.ipynb +++ b/tutorials/tutorial9/tutorial.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tutorial: One dimensional Helmotz equation using Periodic Boundary Conditions\n", + "# Tutorial: One dimensional Helmholtz equation using Periodic Boundary Conditions\n", "This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs)\n", - "a one dimensional Helmotz equation with periodic boundary conditions (PBC).\n", + "a one dimensional Helmholtz equation with periodic boundary conditions (PBC).\n", "We will train with standard PINN's training by augmenting the input with\n", - "periodic expasion as presented in [*An expert’s guide to training\n", + "periodic expansion as presented in [*An expert’s guide to training\n", "physics-informed neural networks*](\n", "https://arxiv.org/abs/2308.08468).\n", "\n", @@ -41,7 +41,7 @@ "source": [ "## The problem definition\n", "\n", - "The one-dimensional Helmotz problem is mathematically written as:\n", + "The one-dimensional Helmholtz problem is mathematically written as:\n", "$$\n", "\\begin{cases}\n", "\\frac{d^2}{dx^2}u(x) - \\lambda u(x) -f(x) &= 0 \\quad x\\in(0,2)\\\\\n", @@ -49,17 +49,17 @@ "\\end{cases}\n", "$$\n", "In this case we are asking the solution to be $C^{\\infty}$ periodic with\n", - "period $2$, on the inifite domain $x\\in(-\\infty, \\infty)$. Notice that the\n", - "classical PINN would need inifinite conditions to evaluate the PBC loss function,\n", - "one for each derivative, which is of course infeasable... \n", + "period $2$, on the infinite domain $x\\in(-\\infty, \\infty)$. Notice that the\n", + "classical PINN would need infinite conditions to evaluate the PBC loss function,\n", + "one for each derivative, which is of course infeasible... \n", "A possible solution, diverging from the original PINN formulation,\n", "is to use *coordinates augmentation*. In coordinates augmentation you seek for\n", "a coordinates transformation $v$ such that $x\\rightarrow v(x)$ such that\n", "the periodicity condition $ u^{(m)}(x=0) - u^{(m)}(x=2) = 0 \\quad m\\in[0, 1, \\cdots] $ is\n", "satisfied.\n", "\n", - "For demonstration porpuses the problem specifics are $\\lambda=-10\\pi^2$,\n", - "and $f(x)=-6\\pi^2\\sin(3\\pi x)\\cos(\\pi x)$ which gives a solution that can be\n", + "For demonstration purposes, the problem specifics are $\\lambda=-10\\pi^2$,\n", + "and $f(x)=-6\\pi^2\\sin(3\\pi x)\\cos(\\pi x)$ which give a solution that can be\n", "computed analytically $u(x) = \\sin(\\pi x)\\cos(3\\pi x)$." ] }, @@ -69,11 +69,11 @@ "metadata": {}, "outputs": [], "source": [ - "class Helmotz(SpatialProblem):\n", + "class Helmholtz(SpatialProblem):\n", " output_variables = ['u']\n", " spatial_domain = CartesianDomain({'x': [0, 2]})\n", "\n", - " def helmotz_equation(input_, output_):\n", + " def Helmholtz_equation(input_, output_):\n", " x = input_.extract('x')\n", " u_xx = laplacian(output_, input_, components=['u'], d=['x'])\n", " f = - 6.*torch.pi**2 * torch.sin(3*torch.pi*x)*torch.cos(torch.pi*x)\n", @@ -83,15 +83,15 @@ " # here we write the problem conditions\n", " conditions = {\n", " 'D': Condition(location=spatial_domain,\n", - " equation=Equation(helmotz_equation)),\n", + " equation=Equation(Helmholtz_equation)),\n", " }\n", "\n", - " def helmotz_sol(self, pts):\n", + " def Helmholtz_sol(self, pts):\n", " return torch.sin(torch.pi * pts) * torch.cos(3. * torch.pi * pts)\n", " \n", - " truth_solution = helmotz_sol\n", + " truth_solution = Helmholtz_sol\n", "\n", - "problem = Helmotz()\n", + "problem = Helmholtz()\n", "\n", "# let's discretise the domain\n", "problem.discretise_domain(200, 'grid', locations=['D'])" @@ -101,11 +101,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As usual the Helmotz problem is written in **PINA** code as a class. \n", + "As usual, the Helmholtz problem is written in **PINA** code as a class. \n", "The equations are written as `conditions` that should be satisfied in the\n", "corresponding domains. The `truth_solution`\n", "is the exact solution which will be compared with the predicted one. We used\n", - "latin hypercube sampling for choosing the collocation points." + "Latin Hypercube Sampling for choosing the collocation points." ] }, { @@ -159,11 +159,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As simple as that! Notice in higher dimension you can specify different periods\n", + "As simple as that! Notice that in higher dimension you can specify different periods\n", "for all dimensions using a dictionary, e.g. `periods={'x':2, 'y':3, ...}`\n", "would indicate a periodicity of $2$ in $x$, $3$ in $y$, and so on...\n", "\n", - "We will now sole the problem as usually with the `PINN` and `Trainer` class." + "We will now solve the problem as usually with the `PINN` and `Trainer` class." ] }, { @@ -209,7 +209,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Great, they overlap perfectly! This seeams a good result, considering the simple neural network used to some this (complex) problem. We will now test the neural network on the domain $[-4, 4]$ without retraining. In principle the periodicity should be present since the $v$ function ensures the periodicity in $(-\\infty, \\infty)$." + "Great, they overlap perfectly! This seems a good result, considering the simple neural network used to some this (complex) problem. We will now test the neural network on the domain $[-4, 4]$ without retraining. In principle the periodicity should be present since the $v$ function ensures the periodicity in $(-\\infty, \\infty)$." ] }, { @@ -258,11 +258,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is pretty clear that the network is periodic, with also the error following a periodic pattern. Obviusly a longer training, and a more expressive neural network could improve the results!\n", + "It is pretty clear that the network is periodic, with also the error following a periodic pattern. Obviously a longer training and a more expressive neural network could improve the results!\n", "\n", "## What's next?\n", "\n", - "Nice you have completed the one dimensional Helmotz tutorial of **PINA**! There are multiple directions you can go now:\n", + "Congratulations on completing the one dimensional Helmholtz tutorial of **PINA**! There are multiple directions you can go now:\n", "\n", "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", "\n", @@ -272,6 +272,11 @@ "\n", "4. Many more..." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/tutorials/tutorial9/tutorial.py b/tutorials/tutorial9/tutorial.py index fd8ff11..1c6d72d 100644 --- a/tutorials/tutorial9/tutorial.py +++ b/tutorials/tutorial9/tutorial.py @@ -1,11 +1,11 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial: One dimensional Helmotz equation using Periodic Boundary Conditions +# # Tutorial: One dimensional Helmholtz equation using Periodic Boundary Conditions # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) -# a one dimensional Helmotz equation with periodic boundary conditions (PBC). +# a one dimensional Helmholtz equation with periodic boundary conditions (PBC). # We will train with standard PINN's training by augmenting the input with -# periodic expasion as presented in [*An expert’s guide to training +# periodic expansion as presented in [*An expert’s guide to training # physics-informed neural networks*]( # https://arxiv.org/abs/2308.08468). # @@ -30,7 +30,7 @@ from pina.equation import Equation # ## The problem definition # -# The one-dimensional Helmotz problem is mathematically written as: +# The one-dimensional Helmholtz problem is mathematically written as: # $$ # \begin{cases} # \frac{d^2}{dx^2}u(x) - \lambda u(x) -f(x) &= 0 \quad x\in(0,2)\\ @@ -38,9 +38,9 @@ from pina.equation import Equation # \end{cases} # $$ # In this case we are asking the solution to be $C^{\infty}$ periodic with -# period $2$, on the inifite domain $x\in(-\infty, \infty)$. Notice that the -# classical PINN would need inifinite conditions to evaluate the PBC loss function, -# one for each derivative, which is of course infeasable... +# period $2$, on the infinite domain $x\in(-\infty, \infty)$. Notice that the +# classical PINN would need infinite conditions to evaluate the PBC loss function, +# one for each derivative, which is of course infeasible... # A possible solution, diverging from the original PINN formulation, # is to use *coordinates augmentation*. In coordinates augmentation you seek for # a coordinates transformation $v$ such that $x\rightarrow v(x)$ such that @@ -54,11 +54,11 @@ from pina.equation import Equation # In[2]: -class Helmotz(SpatialProblem): +class Helmholtz(SpatialProblem): output_variables = ['u'] spatial_domain = CartesianDomain({'x': [0, 2]}) - def helmotz_equation(input_, output_): + def Helmholtz_equation(input_, output_): x = input_.extract('x') u_xx = laplacian(output_, input_, components=['u'], d=['x']) f = - 6.*torch.pi**2 * torch.sin(3*torch.pi*x)*torch.cos(torch.pi*x) @@ -68,21 +68,21 @@ class Helmotz(SpatialProblem): # here we write the problem conditions conditions = { 'D': Condition(location=spatial_domain, - equation=Equation(helmotz_equation)), + equation=Equation(Helmholtz_equation)), } - def helmotz_sol(self, pts): + def Helmholtz_sol(self, pts): return torch.sin(torch.pi * pts) * torch.cos(3. * torch.pi * pts) - truth_solution = helmotz_sol + truth_solution = Helmholtz_sol -problem = Helmotz() +problem = Helmholtz() # let's discretise the domain problem.discretise_domain(200, 'grid', locations=['D']) -# As usual the Helmotz problem is written in **PINA** code as a class. +# As usual the Helmholtz problem is written in **PINA** code as a class. # The equations are written as `conditions` that should be satisfied in the # corresponding domains. The `truth_solution` # is the exact solution which will be compared with the predicted one. We used @@ -129,7 +129,7 @@ model = torch.nn.Sequential(PeriodicBoundaryEmbedding(input_dimension=1, # # We will now sole the problem as usually with the `PINN` and `Trainer` class. -# In[5]: +# In[ ]: pinn = PINN(problem=problem, model=model) @@ -180,7 +180,7 @@ with torch.no_grad(): # # ## What's next? # -# Nice you have completed the one dimensional Helmotz tutorial of **PINA**! There are multiple directions you can go now: +# Nice you have completed the one dimensional Helmholtz tutorial of **PINA**! There are multiple directions you can go now: # # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy #