AmeyaJagtap/Conservative_PINNs
We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.
This project helps researchers and engineers solve complex physics problems governed by conservation laws, like fluid dynamics or heat transfer. It takes the mathematical formulation of these laws and uses a specialized neural network approach to output numerical solutions, even for challenging forward and inverse problems. Computational scientists, physicists, and engineers working with differential equations would find this valuable.
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Use this if you need to solve nonlinear conservation laws, such as the Burgers, Navier-Stokes, or Euler equations, and want to leverage the benefits of domain decomposition and neural networks for potentially faster and more flexible computation.
Not ideal if you are looking for a pre-packaged simulation tool without engaging directly with the underlying mathematical models and neural network architecture.
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Language
Python
License
MIT
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Last pushed
Feb 01, 2023
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