:html_theme.sidebar_secondary.remove: Welcome to PINA’s documentation! ======================================= .. grid:: 6 :gutter: 1 .. grid-item:: .. image:: index_files/tutorial_13_3.png :target: tutorial2/tutorial.html .. grid-item:: .. image:: index_files/tutorial_32_0.png :target: tutorial4/tutorial.html .. grid-item:: .. image:: index_files/tutorial_13_01.png :target: tutorial9/tutorial.html .. grid-item:: .. image:: index_files/tutorial_36_0.png :target: tutorial6/tutorial.html .. grid-item:: .. image:: index_files/tutorial_15_0.png :target: tutorial13/tutorial.html .. grid-item:: .. image:: index_files/tutorial_5_0.png :target: tutorial10/tutorial.html .. grid:: 1 1 3 3 .. grid-item:: :columns: 12 12 8 8 Physics Informed Neural network for Advanced modeling (**PINA**) is an open-source Python library providing an intuitive interface for solving differential equations using PINNs, NOs or both together. Based on `PyTorch `_, `PyTorchLightning `_, and `PyG `_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem and solve it using neural networks . The approximated solution of a differential equation can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface. For further information or questions about **PINA** contact us by email. .. grid-item-card:: Contents :class-title: sd-fs-5 :class-body: sd-pl-4 .. toctree:: :maxdepth: 1 API <_rst/_code> Tutorial <_tutorial> Installing <_installation> Team & Foundings <_team.rst> Contributing <_contributing> License <_LICENSE.rst> Cite PINA <_cite.rst>