Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 15, 2016, Pages 3932-3937

  Brunton, Steven L ^a   Proctor, Joshua L ^b   Kutz, J Nathan ^c  

^a University of Washington   (United States)

^b INSTITUTE FOR DISEASE MODELING   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Dynamical systems; Machine learning; Optimization; Sparse regression; System identification

Indexed keywords

ARTICLE; CHEMICAL REACTION; DYNAMICS; MACHINE LEARNING; MECHANICS; NOISE; OSCILLATION; OSCILLATOR; PRIORITY JOURNAL;

EID: 84994491769     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1517384113     Document Type: Article

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