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Steven L. Brunton
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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
36312016
Machine learning for fluid mechanics
SL Brunton, BR Noack, P Koumoutsakos
Annual review of fluid mechanics 52, 477-508, 2020
20642020
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
19442014
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2019
17322019
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
15442016
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
14912017
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
13602017
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
10932018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
9322016
Data-driven discovery of coordinates and governing equations
K Champion, B Lusch, JN Kutz, SL Brunton
Proceedings of the National Academy of Sciences 116 (45), 22445-22451, 2019
6952019
Closed-loop turbulence control: Progress and challenges
SL Brunton, BR Noack
Applied Mechanics Reviews 67 (5), 050801, 2015
5712015
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
5332018
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
5322016
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2022
5252022
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature Communications 8 (19), 1--9, 2017
5182017
Modal analysis of fluid flows: Applications and outlook
K Taira, MS Hemati, SL Brunton, Y Sun, K Duraisamy, S Bagheri, ...
AIAA journal 58 (3), 998-1022, 2020
4462020
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
4342010
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
3912016
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
3822016
Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
K Manohar, BW Brunton, JN Kutz, SL Brunton
IEEE Control Systems Magazine 38 (3), 63-86, 2018
3762018
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