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Article overview
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Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization | | Dimitris Bertsimas
; Wes Gurnee
; | | Date: |
1 Jun 2022 | | Abstract: | 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, we illustrate the dramatic
improvement of our approach in accurate model discovery while being more sample
efficient, robust to noise, and flexible in accommodating physical constraints. | | Source: | arXiv, 2206.00176 | | Services: | Forum | Review | PDF | Favorites | |
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