Learning Dynamics of Nonlinear Field-Circuit Coupled Problems With a Physics-Data Combined Model

Abstract

This work introduces a combined model that integrates a linear state-space model with a Koopman-type machine-learning model to efficiently predict the dynamics of nonlinear, high-dimensional, and field-circuit coupled systems, as encountered in areas such as electromagnetic compatibility, power electronics, and electric machines. Using an extended nonintrusive model combination algorithm, the proposed model achieves high accuracy with an error of approximately 1%, outperforming baselines: a state-space model and a purely data-driven model. Moreover, it delivers a computational speed-up of three orders of magnitude compared with the traditional time-stepping volume integral method on the same mesh in the online prediction stage, at the cost of a one-time training effort and previously mentioned error, making it highly effective for real-time and repeated predictions.