HiPerSimLab/PECANN
PECANNs: Physics and Equality Constrained Artificial Neural Networks
This method helps researchers and engineers solve complex physics-based problems described by partial differential equations (PDEs), even with limited or varied data. It takes in observational data and the mathematical equations governing a system, then accurately predicts unknown system behaviors or identifies hidden parameters. Scientists, computational fluid dynamics engineers, and material scientists would find this particularly useful.
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Use this if you need highly accurate solutions for forward or inverse problems involving multi-dimensional PDEs, especially when you have multi-fidelity data and need to strictly enforce physical laws and boundary conditions.
Not ideal if your problem doesn't involve physics-based differential equations or if you are looking for a simple, out-of-the-box solution without needing to understand constrained optimization.
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Jupyter Notebook
License
MIT
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Last pushed
Jun 05, 2023
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