0c8072274e
* Tutorial doc update * update doc tutorial * doc not compiling --------- Co-authored-by: Dario Coscia <dcoscia@euclide.maths.sissa.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
75 lines
3.1 KiB
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75 lines
3.1 KiB
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Welcome to PINA's documentation!
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===================================================
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.. figure:: index_files/pina_logo.png
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:align: center
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:width: 150
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Physics Informed Neural network for Advanced modeling (**PINA**) is
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an open-source Python library providing an intuitive interface for
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solving differential equations using PINNs, NOs or both together.
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Based on `PyTorch <https://pytorch.org/>`_ and `PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_,
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PINA offers a simple and intuitive way to formalize a specific (differential) problem
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and solve it using neural networks . The approximated solution of a differential equation
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can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.
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`PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_ as backhand is done to offer
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professional AI researchers and machine learning engineers the possibility of using advancement
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training strategies provided by the library, such as multiple device training, modern model compression techniques,
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gradient accumulation, and so on. In addition, it provides the possibility to add arbitrary
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self-contained routines (callbacks) to the training for easy extensions without the need to touch the
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underlying code.
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The high-level structure of the package is depicted in our API. The pipeline to solve differential equations
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with PINA follows just five steps: problem definition, model selection, data generation, solver selection, and training.
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.. figure:: index_files/API_color.png
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:alt: PINA application program interface
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:align: center
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:width: 500
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Physics-informed neural network
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`PINN <https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125>`_ is a novel approach that
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involves neural networks to solve differential equations in an unsupervised manner, while respecting
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any given law of physics described by general differential equations. Proposed in "*Physics-informed neural
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networks: A deep learning framework for solving forward and inverse problems
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involving nonlinear partial differential equations*", such framework aims to
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solve problems in a continuous and nonlinear settings.
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Neural operator learning
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`Neural Operators <https://www.jmlr.org/papers/v24/21-1524.html>`_ is a novel approach involving neural networks
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to learn differential operators using supervised learning strategies. By learning the differential operator, the
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neural network is able to generalize across different instances of the differential equations (e.g. different forcing
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terms), without the need of re-training.
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.. toctree::
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:maxdepth: 2
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:caption: Package Documentation:
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API <_rst/_code>
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Contributing <_rst/_contributing>
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License <_LICENSE.rst>
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.. the following is demo content intended to showcase some of the features you can invoke in reStructuredText
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.. this can be safely deleted or commented out
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.. ........................................................................................
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.. toctree::
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:maxdepth: 1
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:numbered:
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:caption: Getting Started:
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Installation <_rst/_installation>
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Tutorials <_rst/_tutorials>
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