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Automatically discovering ordinary differential equations from data with sparse regression

Engineering and Technology

Automatically discovering ordinary differential equations from data with sparse regression

K. Egan, W. Li, et al.

Discover how Kevin Egan, Weizhen Li, and Rui Carvalho are transforming the identification of nonlinear differential equations from data! Their innovative methodology combines denoising, sparse regression, and bootstrapping, allowing for automated discovery of dynamical laws with minimal manual tuning. This research has the potential to revolutionize our understanding of complex systems.

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Playback language: English
Abstract
Discovering nonlinear differential equations from data is crucial in science. Existing methods often require manual hyperparameter tuning. This paper proposes a methodology integrating denoising, sparse regression, and bootstrapping to automatically identify dynamical laws. Evaluated on known ODEs with varied conditions, the algorithm consistently identifies three-dimensional systems with moderately sized time series and high signal quality. This automated approach promises to improve understanding of complex systems.
Publisher
Communications Physics
Published On
Jan 01, 2024
Authors
Kevin Egan, Weizhen Li, Rui Carvalho
Tags
nonlinear differential equations
data analysis
automated methodology
dynamical laws
sparse regression
denoising
bootstrapping

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