PhICNet: Physics-Incorporated Convolutional Recurrent Neural Networks for Modeling Dynamical Systems

Dynamical systems involving partial differential equations (PDEs) and ordinary differential equations (ODEs) arise in many fields of science and engineering. In this paper, we present a physics-incorporated deep learning framework to model and predict the spatiotemporal evolution of dynamical systems governed by partially-known inhomogenous PDEs with unobservable source dynamics. We formulate our model PhICNet as a convolutional recurrent neural network which is end-to-end trainable for spatiotemporal evolution prediction of dynamical systems. Experimental results show the long-term prediction capability of our model.