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fix tutorials latex and links (#261)

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This commit is contained in:
Dario Coscia
2024-03-10 10:23:35 +01:00
committed by GitHub
parent 4f911f88ca
commit ada9643c11
15 changed files with 84 additions and 284 deletions
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docs/source/_rst/tutorials/tutorial1/tutorial.rst
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@@ -28,8 +28,12 @@ Build a PINA problem
Problem definition in the **PINA** framework is done by building a
python ``class``, which inherits from one or more problem classes
(``SpatialProblem``, ``TimeDependentProblem``, ``ParametricProblem``, …)
depending on the nature of the problem. Below is an example: ### Simple
Ordinary Differential Equation Consider the following:
depending on the nature of the problem. Below is an example:
Simple Ordinary Differential Equation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Consider the following:
.. math::
@@ -83,12 +87,6 @@ will inherit from both ``SpatialProblem`` and ``TimeDependentProblem``:
# 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.
@@ -96,12 +94,12 @@ In summary, using **PINA**, we can initialize a problem with a class
which inherits from different base classes: ``SpatialProblem``,
``TimeDependentProblem``, ``ParametricProblem``, and so on depending on
the type of problem we are considering. Here are some examples (more on
the official documentation): \* ``SpatialProblem`` :math:`\rightarrow` a
differential equation with spatial variable(s) \*
``TimeDependentProblem`` :math:`\rightarrow` a time-dependent
differential equation \* ``ParametricProblem`` :math:`\rightarrow` a
parametrized differential equation \* ``AbstractProblem``
:math:`\rightarrow` any **PINA** problem inherits from here
the official documentation):
* ``SpatialProblem`` :math:`\rightarrow` a differential equation with spatial variable(s) ``spatial_domain``
* ``TimeDependentProblem`` :math:`\rightarrow` a time-dependent differential equation with temporal variable(s) ``temporal_domain``
* ``ParametricProblem`` :math:`\rightarrow` a parametrized differential equation with parametric variable(s) ``parameter_domain``
* ``AbstractProblem`` :math:`\rightarrow` any **PINA** problem inherits from here
Write the problem class
~~~~~~~~~~~~~~~~~~~~~~~
@@ -300,31 +298,6 @@ If you want to track the metric by yourself without a logger, use
# train
trainer.train()
.. parsed-literal::
GPU available: False, used: False
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: | | 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::
`Trainer.fit` stopped: `max_epochs=1500` reached.
.. parsed-literal::
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
**PINA** logs mean square error residual loss). The logged metrics can
be accessed online using one of the ``Lightinig`` loggers. The final
@@ -355,11 +328,6 @@ quatitative plots of the solution.
pl.plot(solver=pinn)
.. 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.
.. image:: tutorial_files/tutorial_23_1.png