J. Nathan Kutz

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Steven L. BruntonProfessor, University of WashingtonPatvirtintas el. paštas uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates FoundationPatvirtintas el. paštas idmod.org
Bingni W. BruntonProfessor, Richard and Joan Komen University Chair, University of Washington, SeattlePatvirtintas el. paštas uw.edu
Eurika KaiserUniversity of WashingtonPatvirtintas el. paštas uw.edu
Krithika ManoharUniversity of WashingtonPatvirtintas el. paštas uw.edu
N. Benjamin ErichsonResearch ScientistPatvirtintas el. paštas lbl.gov
Xing FuAnt GroupPatvirtintas el. paštas antgroup.com
Keren BergmanColumbia UniversityPatvirtintas el. paštas ee.columbia.edu
PKA WaiThe Hong Kong Polytechnic UniversityPatvirtintas el. paštas polyu.edu.hk
Matthew WilliamsOceanitPatvirtintas el. paštas oceanit.com
Bethany LuschArgonne National LabPatvirtintas el. paštas anl.gov
Jonathan H. TuPacific Northwest National LaboratoryPatvirtintas el. paštas pnnl.gov
Jared BronskiUniversity of IllinoisPatvirtintas el. paštas math.uiuc.edu
Steven CundiffPhysics Department, University of Michigan, Ann Arbor, MI 48109Patvirtintas el. paštas umich.edu
Pedro Doria MaiaAssistant Professor of Mathematics, UT ArlingtonPatvirtintas el. paštas uta.edu
Niall M ManganAssistant Professor of Engineering Sciences and Applied Math, Northwestern UniversityPatvirtintas el. paštas northwestern.edu
Feng LiThe Hong Kong Polytechnic UniversityPatvirtintas el. paštas polyu.edu.hk
Travis AskhamAssistant Professor, New Jersey Institute of TechnologyPatvirtintas el. paštas njit.edu
Jeffrey A RiffellUniversity of WashingtonPatvirtintas el. paštas uw.edu
William L. KathNorthwestern UniversityPatvirtintas el. paštas northwestern.edu

Stebėti

J. Nathan Kutz

Professor of Applied Mathematics & Electrical and Computer Engineering

Patvirtintas el. paštas uw.edu - Pagrindinis puslapis

Dynamical Systems Data Science Machine Learning Optics Neuroscience


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	4878	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	3122	2022
On dynamic mode decomposition: Theory and applications JH Tu, CW Rowley, DM Luchtenberg, SL Brunton SL, JN Kutz Journal of Computational Dynamics 1 (2), 391-421, 2014	2322	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	1955	2016
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017	1684	2017
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018	1455	2018
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016	1192	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	944	2019
Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017	897	2017
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	712	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	668	2016
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017	648	2017
Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013	566	2013
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021	550	2021
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition BW Brunton, LA Johnson, JG Ojemann, JN Kutz Journal of neuroscience methods 258, 1-15, 2016	546	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	490	2018
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	480	2016
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016	458	2016
Data-driven discovery of Koopman eigenfunctions for control E Kaiser, JN Kutz, SL Brunton Machine Learning: Science and Technology 2 (3), 035023, 2021	425	2021
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	421	2018

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