Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization.

ABSTRACT: Discovering the governing laws underpinning physical and chemical phenomena entirely from data is a key step towards understanding and ultimately controlling systems in science and engineering. Noisy measurements and complex, highly nonlinear underlying dynamics hinder the identification of such governing laws. In this work, we introduce a machine learning framework rooted in moving horizon nonlinear optimization for identifying governing equations in the form of ordinary differential equations from noisy experimental data sets. Our approach evaluates sequential subsets of measurement data, and exploits statistical arguments to learn truly parsimonious governing equations from a large dictionary of basis functions. The proposed framework reduces gradient approximation errors by implicitly

SUBMITTER: Lejarza F 

PROVIDER: S-EPMC9276674 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature