Steven L. Brunton

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J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringPatvirtintas el. paštas uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates FoundationPatvirtintas el. paštas idmod.org
Bernd R. NoackNational Talent Professor at Harbin Institute of Technology; Professor at TU BerlinPatvirtintas el. paštas hit.edu.cn
Eurika KaiserUniversity of WashingtonPatvirtintas el. paštas uw.edu
Bingni W. BruntonProfessor, Richard and Joan Komen University Chair, University of Washington, SeattlePatvirtintas el. paštas uw.edu
Clarence W RowleyProfessor of Mechanical and Aerospace Engineering, Princeton UniversityPatvirtintas el. paštas princeton.edu
Kunihiko TairaUniversity of California, Los AngelesPatvirtintas el. paštas seas.ucla.edu
Beverley McKeonStanford UniversityPatvirtintas el. paštas stanford.edu
Krithika ManoharUniversity of WashingtonPatvirtintas el. paštas uw.edu
Scott DawsonIllinois Institute of TechnologyPatvirtintas el. paštas iit.edu
N. Benjamin ErichsonResearch ScientistPatvirtintas el. paštas lbl.gov
Jean-Christophe LoiseauAssistant Professor, DynFluid Laboratory - Arts et Métiers ParisTechPatvirtintas el. paštas ensam.eu
Jared CallahamUniversity of WashingtonPatvirtintas el. paštas uw.edu
Urban FaselImperial College LondonPatvirtintas el. paštas imperial.ac.uk
Alan Ali KaptanogluAssistant Professor, New York UniversityPatvirtintas el. paštas nyu.edu
Brian PolagyeProfessor of Mechanical Engineering, University of WashingtonPatvirtintas el. paštas uw.edu
Thomas DuriezCONICETPatvirtintas el. paštas fi.uba.ar
Benjamin HerrmannUniversidad de ChilePatvirtintas el. paštas uchile.cl
Bethany LuschArgonne National LabPatvirtintas el. paštas anl.gov
Aleksandr AravkinUniversity of WashingtonPatvirtintas el. paštas uw.edu

Stebėti

Steven L. Brunton

Professor, University of Washington

Patvirtintas el. paštas uw.edu - Pagrindinis puslapis

Dynamical systems Control Fluid dynamics Machine learning Model Reduction


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	4591	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	2896	2022
Machine learning for fluid mechanics SL Brunton, BR Noack, P Koumoutsakos Annual review of fluid mechanics 52 (1), 477-508, 2020	2616	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	2242	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	1872	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	1777	2017
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017	1617	2017
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018	1381	2018
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016	1141	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	888	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	671	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	611	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	530	2020
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021	499	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	467	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	461	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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