📝 SummarXiv

Abstract

Learning the governing equations of dynamical systems from data has drawn significant attention across diverse fields, including physics, engineering, robotics and control, economics, climate science, and healthcare. Sparse regression techniques, exemplified by the Automatic Regression for Governing Equations (ARGOS) framework, have demonstrated effectiveness in extracting parsimonious models from time series data. However, real-world dynamical systems are driven by input control, external forces, or human interventions, which standard ARGOS does not accommodate. To address this, we introduce ARGOS with control (ARGOSc), an extension of ARGOS that incorporates external control inputs into the system identification process. ARGOSc extends the sparse regression framework to infer governing equations while accounting for the effects of exogenous inputs, enabling robust identification of forcing dynamics in low- to medium-noise datasets. We demonstrate ARGOSc efficacy on benchmark systems, including the Van der Pol oscillator, Lotka-Volterra, and the Lorenz system with forcing and feedback control, showing enhanced accuracy in discovering governing laws. Under the noisy conditions, ARGOSc outperforms the widely used sparse identification of nonlinear dynamics with control (SINDYc), in accurately identifying the underlying forced dynamics. In some cases, SINDYc fails to capture the true system dynamics, whereas ARGOSc consistently succeeds.