zhili93/deep-learning-framework-for-pdes

deep learning framework

deep-learning-framework-for-PDEs

In this project, we have implemented a physics-informed neural network framework capable of solving the forward and inverse problem for non-linear partial differential equations [1].

Directory

-Utilities (contains useful plotting function) 

-Main

    -forward_problem 

        -MySch_Final.py (TensorFlow 2.0 implementation for forward problem)

        -Data (training data for the forward problem)

        -Output (outputs from the neural network model)

    -inverse_problem

        -NavierStokes_tf2.py (TensorFlow 2.0 implementation for inverse problem)

        -Data (training data for the inverse problem)

        -Output (outputs from the neural network model)

Reference: [1] Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.