caio-davi/PSO-PINN
Physics-Informed Neural Networks Trained with Particle Swarm Optimization
This project helps engineers and scientists more accurately solve complex physics problems modeled by partial differential equations using deep learning. It takes in your differential equation model and relevant data, and outputs a robust, more reliable solution with quantified uncertainty. Researchers and practitioners in fields like fluid dynamics, heat transfer, or materials science who use Physics-Informed Neural Networks (PINNs) will find this useful.
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Use this if you are developing or applying Physics-Informed Neural Networks (PINNs) and are encountering issues with training stability, slow convergence, or require a better understanding of the uncertainty in your model's predictions.
Not ideal if you are looking for a general-purpose machine learning framework for problems not involving partial differential equations, or if you prefer pure gradient-based optimization without an ensemble approach.
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Language
Python
License
MIT
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Last pushed
Sep 20, 2022
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