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Generalizing the SINDy approach with nested neural networks
April 25, 2024, 7:43 p.m. | Camilla Fiorini, Cl\'ement Flint, Louis Fostier, Emmanuel Franck, Reyhaneh Hashemi, Victor Michel-Dansac, Wassim Tenachi
cs.LG updates on arXiv.org arxiv.org
Abstract: Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (\sindy) framework, which uses sparse regression to identify governing equations from data. This study introduces an enhanced method, Nested SINDy, that aims to increase the expressivity of the SINDy approach thanks to a nested structure. Indeed, traditional symbolic regression and system identification methods often …
abstract arxiv cs.lg cs.na data framework identification identify math.na networks neural networks popular regression research study systems type
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