Steven L. Brunton
Cited by
Cited by
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
SL Brunton, JL Proctor, JN Kutz
Proceedings of the national academy of sciences 113 (15), 3932-3937, 2016
On dynamic mode decomposition: Theory and applications
JH Tu, CW Rowley, DM Luchtenburg, SL Brunton, JN Kutz
Journal of Computational Dynamics 1 (2), 391-421, 2014
Modal analysis of fluid flows: An overview
K Taira, SL Brunton, STM Dawson, CW Rowley, T Colonius, BJ McKeon, ...
Aiaa Journal 55 (12), 4013-4041, 2017
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science Advances 3 (4), e1602614, 2017
Machine learning for fluid mechanics
SL Brunton, BR Noack, P Koumoutsakos
Annual Review of Fluid Mechanics 52, 477-508, 2020
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control
SL Brunton, CW Rowley, SR Kulkarni, C Clarkson
Power Electronics, IEEE Transactions on 25 (10), 2531-2540, 2010
Closed-loop turbulence control: Progress and challenges
SL Brunton, BR Noack
Applied Mechanics Reviews 67 (5), 2015
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 1-10, 2018
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2019
Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
SL Brunton, BW Brunton, JL Proctor, JN Kutz
PloS one 11 (2), e0150171, 2016
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature Communications 8 (19), 1--9, 2017
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
Machine Learning Control - Taming Nonlinear Dynamics and Turbulence
BRN Thomas Duriez, Steven L. Brunton
Springer-Verlag, Series 'Fluid Mechanics and Its Applications' 116, 246, 2016
Inferring biological networks by sparse identification of nonlinear dynamics
NM Mangan, SL Brunton, JL Proctor, JN Kutz
IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2 …, 2016
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
E Kaiser, JN Kutz, SL Brunton
Proceedings of the Royal Society A 474 (2219), 20180335, 2018
Constrained sparse Galerkin regression
JC Loiseau, SL Brunton
Journal of Fluid Mechanics 838, 42-67, 2018
Model selection for dynamical systems via sparse regression and information criteria
NM Mangan, JN Kutz, SL Brunton, JL Proctor
Proceedings of the Royal Society A 473 (2204), 1--16, 2017
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, S Brunton
Machine Learning: Science and Technology, 2021
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