PDE N ET 2 0 L EARNING PDE S FROM D ATA WITH A N UMERIC S YMBOLIC H YBRID D EEP N ETWORK
Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE Net 2.0, to discover (time dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE Net 2.0 is based on our earlier work [1] where the original version of PDE Net was proposed. PDE Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi layer neural network for model recovery. Comparing with existing approaches, PDE Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.