AmeyaJagtap/XPINNs_TensorFlow-2
XPINN code written in TensorFlow 2
This project offers a method to solve complex physics problems described by nonlinear partial differential equations (PDEs) across various fields like engineering or physical sciences. It takes your PDE problem, potentially with intricate geometries or time-dependent behavior, and uses multiple neural networks to efficiently find solutions. Researchers, scientists, and engineers who work with modeling physical systems using PDEs would find this beneficial.
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Use this if you need to solve challenging forward or inverse nonlinear partial differential equations on complex or arbitrarily decomposed space-time domains, and want to leverage parallel computation for faster results.
Not ideal if your problem involves simple, linear PDEs or if you prefer traditional numerical solvers over deep learning approaches.
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Feb 01, 2023
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