78 lines
2.5 KiB
ReStructuredText
78 lines
2.5 KiB
ReStructuredText
:html_theme.sidebar_secondary.remove:
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Welcome to PINA's documentation!
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=======================================
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.. grid:: 6
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:gutter: 1
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.. grid-item::
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.. image:: index_files/tutorial_13_3.png
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:target: tutorial2/tutorial.html
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.. image:: index_files/tutorial_15_0.png
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:target: tutorial10/tutorial.html
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.. grid:: 1 1 3 3
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.. grid-item::
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:columns: 12 12 8 8
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**PINA** is an open-source Python library designed to simplify and accelerate
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the development of Scientific Machine Learning (SciML) solutions.
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Built on top of `PyTorch <https://pytorch.org/>`_, `PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>`_,
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and `PyTorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/>`_,
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PINA provides an intuitive framework for defining, experimenting with,
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and solving complex problems using Neural Networks,
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Physics-Informed Neural Networks (PINNs), Neural Operators, and more.
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- **Modular Architecture**: Designed with modularity in mind and relying on powerful yet composable abstractions, PINA allows users to easily plug, replace, or extend components, making experimentation and customization straightforward.
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- **Scalable Performance**: With native support for multi-device training, PINA handles large datasets efficiently, offering performance close to hand-crafted implementations with minimal overhead.
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- **Highly Flexible**: Whether you're looking for full automation or granular control, PINA adapts to your workflow. High-level abstractions simplify model definition, while expert users can dive deep to fine-tune every aspect of the training and inference process.
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For further information or questions about **PINA** contact us by email.
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.. grid-item-card:: Contents
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:class-title: sd-fs-5
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:class-body: sd-pl-4
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.. toctree::
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:maxdepth: 1
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Installing <_installation>
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API <_rst/_code>
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Tutorials <_tutorial>
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Cite PINA <_cite.rst>
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Contributing <_contributing>
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Team & Foundings <_team.rst>
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License <_LICENSE.rst>
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