Lawrence Berkeley National Laboratory

Differential equations based on physical principals are used to represent complex dynamic systems in all fields of science and engineering. When known, these equations have been shown to well represent real-world dynamics. However, since the true dynamics of complex systems are generally unknown, learning the governing equations can improve our understanding of the mechanisms driving the systems. Here, we develop a Bayesian approach to data-driven discovery of nonlinear spatio-temporal dynamic equations. Our approach can accommodate measurement error and missing data, both of which are common in real-world data, and accounts for parameter uncertainty. The proposed framework is illustrated using three simulated systems with varying amounts of measurement uncertainty and missing data and applied to a real-world system to infer the temporal evolution of the vorticity of the streamfunction.