Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identificat...

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Bibliographic Details

Published in:	Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences Vol. 478; no. 2260; p. 20210904
Main Authors:	Fasel, U, Kutz, J N, Brunton, B W, Brunton, S L
Format:	Journal Article
Language:	English
Published:	England 01.04.2022
Subjects:	active learning model discovery nonlinear dynamics probabilistic forecasting ensemble methods sparse regression
ISSN:	1364-5021
Online Access:	Get more information
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