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Welcome to PINA's documentation!
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    **PINA** is an open-source Python library designed to simplify and accelerate 
    the development of Scientific Machine Learning (SciML) solutions.
    Built on top of `PyTorch <https://pytorch.org/>`_, `PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>`_, 
    and `PyTorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/>`_, 
    PINA provides an intuitive framework for defining, experimenting with, 
    and solving complex problems using Neural Networks, 
    Physics-Informed Neural Networks (PINNs), Neural Operators, and more.

    - **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.

    - **Scalable Performance**: With native support for multi-device training, PINA handles large datasets efficiently, offering performance close to hand-crafted implementations with minimal overhead.

    - **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.

    For further information or questions about **PINA** contact us by email. 
    
  .. grid-item-card:: Contents
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      Installing <_installation>
      API <_rst/_code>
      Tutorials <_tutorial>
      Cite PINA <_cite.rst>
      Contributing <_contributing>
      Team & Foundings <_team.rst>
      License <_LICENSE.rst>