Work

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Year: 2024

Type: article

Abstract: Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024

Source: Physical Review Research

DOI: 10.1103/physrevresearch.6.043062

Authors: George Stepaniants, Alasdair D. Hastewell, Dominic J. Skinner, Jan F. Totz, Jörn Dunkel

Institutions: California Institute of Technology, Massachusetts Institute of Technology, Northwestern University

Language: English

Topic: Chaos control and synchronization

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: National Science Foundation, Alfred P. Sloan Foundation, Alexander von Humboldt-Stiftung