Articles with public access mandates - Steven L. BruntonLearn more
Available somewhere: 165
Machine learning for fluid mechanics
SL Brunton, BR Noack, P Koumoutsakos
Annual review of fluid mechanics 52 (1), 477-508, 2020
Mandates: Swiss National Science Foundation, US Department of Defense, German Research …
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
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
Mandates: US Department of Defense
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
Mandates: US Department of Defense
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
Mandates: US National Science Foundation, US Department of Energy, US Department of …
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
Mandates: US Department of Defense, Gordon and Betty Moore Foundation
Closed-loop turbulence control: Progress and challenges
SL Brunton, BR Noack
Applied Mechanics Reviews 67 (5), 050801, 2015
Mandates: US Department of Energy, German Research Foundation
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature Communications 8 (19), 1--9, 2017
Mandates: US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US Department of Defense
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, SL Brunton
Machine Learning: Science and Technology 2 (3), 035023, 2021
Mandates: US National Science Foundation, US Department of Defense
Generalizing Koopman theory to allow for inputs and control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 17 (1), 909-930, 2018
Mandates: Bill & Melinda Gates Foundation, US Department of Defense
Enhancing computational fluid dynamics with machine learning
R Vinuesa, SL Brunton
Nature Computational Science 2 (6), 358-366, 2022
Mandates: US Department of Defense, Swedish Research Council, European Commission
Constrained sparse Galerkin regression
JC Loiseau, SL Brunton
Journal of Fluid Mechanics 838, 42-67, 2018
Mandates: US Department of Defense
Data-driven identification of parametric partial differential equations
S Rudy, A Alla, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 18 (2), 643-660, 2019
Mandates: US Department of Defense
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
K Kaheman, JN Kutz, SL Brunton
Proceedings of the Royal Society A 476 (2242), 20200279, 2020
Mandates: US Department of Defense
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
Mandates: US Department of Defense
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
U Fasel, JN Kutz, BW Brunton, SL Brunton
Proceedings of the Royal Society A 478 (2260), 20210904, 2022
Mandates: US National Science Foundation, US Department of Defense
Shallow neural networks for fluid flow reconstruction with limited sensors
NB Erichson, L Mathelin, Z Yao, SL Brunton, MW Mahoney, JN Kutz
Proceedings of the Royal Society A 476 (2238), 20200097, 2020
Mandates: US National Science Foundation, US Department of Defense, Agence Nationale …
Data-driven aerospace engineering: reframing the industry with machine learning
SL Brunton, J Nathan Kutz, K Manohar, AY Aravkin, K Morgansen, ...
AIAA Journal 59 (8), 2820-2847, 2021
Mandates: US National Science Foundation
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