PINN(s): Physics-Informed Neural Network(s) for Burgers equation
This is an implementation of PINN(s) on TensorFlow 2 to solve Burgers equation (1D Navier-Stokes eq. with no pressure gradient / external force) under Dirichlet boundary condition w/o training data (data to fit initial & boundary conditions need to be provided). This is keras-utilized version unlike other two of my repos (PINN_wave / PINN_von_Karman).
to run the entire code. Basic parameters (e.g., network architecture, batch size, initializer, etc.) are found in
and could be modified depending on the problem setup.
with the following:
 Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, Vol. 378, pp. 686-707, 2019. (paper)