Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method.

ABSTRACT: The autonomous distillation of physical laws only from data is of great interest in many scientific fields. Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. However, SINDy faces certain difficulties when the dynamics contain rational functions. The Lagrangian is substantially more concise than the actual equations of motion, especially for complex systems, and it does not usually contain rational functions for mechanical systems. Few proposed methods proposed to date, such as Lagrangian-SINDy we have proposed recently, can extract the true form of the Lagrangian of dynamical systems from da

SUBMITTER: Purnomo A 

PROVIDER: S-EPMC10188520 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature