Sparse Identification of Lagrangian for Nonlinear Dynamical Systems via Proximal Gradient Method

TL;DR

This paper introduces xL-SINDy, a method combining SINDy and proximal gradient techniques to accurately and robustly extract Lagrangians from noisy data in nonlinear dynamical systems, outperforming existing methods.

Contribution

The paper develops an extended Lagrangian-SINDy method using proximal gradient for noise robustness, advancing the automatic discovery of physical laws from data.

Findings

xL-SINDy outperforms SINDy-PI in noisy environments by 8-20 times.

Demonstrated effectiveness on four nonlinear systems including pendulums.

Enhanced robustness in extracting Lagrangians from noisy measurements.

Abstract

Distilling physical laws autonomously from data has been of great interest in many scientific areas. The sparse identification of nonlinear dynamics (SINDy) and its variations have been developed to extract the underlying governing equations from observation data. However, SINDy faces certain difficulties when the dynamics contain rational functions. The principle of the least action governs many mechanical systems, mathematically expressed in the Lagrangian formula. Compared to the actual equation of motions, the Lagrangian is much more concise, especially for complex systems, and does not usually contain rational functions for mechanical systems. Only a few methods have been proposed to extract the Lagrangian from measurement data so far. One of such methods, Lagrangian-SINDy, can extract the true form of Lagrangian of dynamical systems from data but suffers when noises are present. In this work, we develop an extended version of Lagrangian-SINDy (xL-SINDy) to obtain the Lagrangian of dynamical systems from noisy measurement data. We incorporate the concept of SINDy and utilize the proximal gradient method to obtain sparse expressions of the Lagrangian. We demonstrated the effectiveness of xL-SINDy against different noise levels with four nonlinear dynamics: a single pendulum, a cart-pendulum, a double pendulum, and a spherical pendulum. Furthermore, we also verified the performance of xL-SINDy against SINDy-PI (parallel, implicit), a recent robust variant of SINDy that can handle implicit dynamics and rational nonlinearities. Our experiment results show that xL-SINDy is 8-20 times more robust than SINDy-PI in the presence of noise.