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Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization. (arXiv:2206.00176v1 [cs.LG])
June 2, 2022, 1:10 a.m. | Dimitris Bertsimas, Wes Gurnee
cs.LG updates on arXiv.org arxiv.org
Discovering governing equations of complex dynamical systems directly from
data is a central problem in scientific machine learning. In recent years, the
sparse identification of nonlinear dynamics (SINDy) framework, powered by
heuristic sparse regression methods, has become a dominant tool for learning
parsimonious models. We propose an exact formulation of the SINDy problem using
mixed-integer optimization (MIO) to solve the sparsity constrained regression
problem to provable optimality in seconds. On a large number of canonical
ordinary and partial differential equations, …
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