Steven L. Brunton

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	すべて	2019 年以来
引用	42131	38859
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J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer Engineering確認したメールアドレス: uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates Foundation確認したメールアドレス: idmod.org
Bernd R. NoackNational Talent Professor at Harbin Institute of Technology; Professor at TU Berlin確認したメールアドレス: hit.edu.cn
Eurika KaiserUniversity of Washington確認したメールアドレス: uw.edu
Bingni W. BruntonProfessor, Richard and Joan Komen University Chair, University of Washington, Seattle確認したメールアドレス: uw.edu
Clarence W RowleyProfessor of Mechanical and Aerospace Engineering, Princeton University確認したメールアドレス: princeton.edu
Kunihiko TairaUniversity of California, Los Angeles確認したメールアドレス: seas.ucla.edu
Beverley McKeonStanford University確認したメールアドレス: stanford.edu
Krithika ManoharUniversity of Washington確認したメールアドレス: uw.edu
Scott DawsonIllinois Institute of Technology確認したメールアドレス: iit.edu
N. Benjamin ErichsonResearch Scientist確認したメールアドレス: lbl.gov
Jean-Christophe LoiseauAssistant Professor, DynFluid Laboratory - Arts et Métiers ParisTech確認したメールアドレス: ensam.eu
Jared CallahamUniversity of Washington確認したメールアドレス: uw.edu
Urban FaselImperial College London確認したメールアドレス: imperial.ac.uk
Alan Ali KaptanogluAssistant Professor, New York University確認したメールアドレス: nyu.edu
Brian PolagyeProfessor of Mechanical Engineering, University of Washington確認したメールアドレス: uw.edu
Thomas DuriezCONICET確認したメールアドレス: fi.uba.ar
Benjamin HerrmannUniversidad de Chile確認したメールアドレス: uchile.cl
Bethany LuschArgonne National Lab確認したメールアドレス: anl.gov
Aleksandr AravkinUniversity of Washington確認したメールアドレス: uw.edu

フォロー

Steven L. Brunton

Professor, University of Washington

確認したメールアドレス: uw.edu - ホームページ

Dynamical systems Control Fluid dynamics Machine learning Model Reduction


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	4603	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	2905	2022
Machine learning for fluid mechanics SL Brunton, BR Noack, P Koumoutsakos Annual review of fluid mechanics 52 (1), 477-508, 2020	2622	2020
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	2246	2014
Dynamic mode decomposition: data-driven modeling of complex systems JN Kutz, SL Brunton, BW Brunton, JL Proctor Society for Industrial and Applied Mathematics, 2016	1873	2016
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	1781	2017
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017	1620	2017
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018	1384	2018
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016	1142	2016
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	890	2019
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	674	2018
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	645	2016
Closed-loop turbulence control: Progress and challenges SL Brunton, BR Noack Applied Mechanics Reviews 67 (5), 050801, 2015	636	2015
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature Communications 8 (19), 1--9, 2017	612	2017
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	531	2020
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021	501	2021
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	469	2016
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	463	2018
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016	443	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	443	2010

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