BGCL:Learning Constitutive Laws for System Identification

Nonlinear system identification of dynamical systems is a challenging problem. Recently, learning based approaches have made attempts to embed physical priors in the learning model to improve model identification of dynamical systems. In this paper, we propose the Bond Graph based Con stitutive Law learning (BGCL) framework to learn analytical expressions for constitutive laws and thus identify models for physical dynamical systems. Simulation studies conducted on a spring mass system and synchronous three phase motor are used to validate the proposed framework.