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Discovering governing equations from data: Sparse identification of nonlinear dynamical systems

Paper title Discovering governing equations from data: Sparse identification of nonlinear dynamical systems

Brunton et al. (University of Washington) · General physics

Paper arXiv 1509.03580PDF

Stacks time histories of a system's state and of its time derivative, builds a library of candidate nonlinear functions of the state (polynomials, for example) and uses sparse regression to keep the fewest library terms that reproduce the derivative. The only assumption is that the governing equations have few terms.

Published
PNAS

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