SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
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Publication:2128325
DOI10.1016/j.jcp.2020.109962OpenAlexW3107102194WikidataQ115350094 ScholiaQ115350094MaRDI QIDQ2128325
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.07788
Related Items (4)
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation ⋮ Data-driven sparse identification of nonlinear dynamical systems using linear multistep methods ⋮ Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data ⋮ Quadrature rule based discovery of dynamics by data-driven denoising
Uses Software
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