Learning the dynamics for unknown hyperbolic conservation laws using deep neural networks

We propose a new data -driven method to learn the dynamics of an unknown hyperbolic system of conservation laws using deep neural networks. Inspired by classical methods in numerical conservation laws, we develop a new conservative form network (CFN) in which the network learns to approximate the numerical flux function of the unknown system. Our numerical examples demonstrate that the CFN yields significantly better prediction accuracy than what is obtained using a standard nonconservative form network, even when it is enhanced with constraints to promote conservation. In particular, solutions obtained using the CFN consistently capture the correct shock propagation speed without introducing nonphysical oscillations into the solution. They are furthermore robust to noisy and sparse observation environments.