J. Nathan Kutz

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Steven L. BruntonProfessor, University of WashingtonVerifierad e-postadress på uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates FoundationVerifierad e-postadress på idmod.org
Bingni W. BruntonProfessor, Richard and Joan Komen University Chair, University of Washington, SeattleVerifierad e-postadress på uw.edu
Eurika KaiserUniversity of WashingtonVerifierad e-postadress på uw.edu
Krithika ManoharUniversity of WashingtonVerifierad e-postadress på uw.edu
N. Benjamin ErichsonResearch ScientistVerifierad e-postadress på lbl.gov
Xing FuAnt GroupVerifierad e-postadress på antgroup.com
Keren BergmanColumbia UniversityVerifierad e-postadress på ee.columbia.edu
PKA WaiThe Hong Kong Polytechnic UniversityVerifierad e-postadress på polyu.edu.hk
Matthew WilliamsOceanitVerifierad e-postadress på oceanit.com
Edwin DingAzusa Pacific UniversityVerifierad e-postadress på apu.edu
Bethany LuschArgonne National LabVerifierad e-postadress på anl.gov
Jared BronskiUniversity of IllinoisVerifierad e-postadress på math.uiuc.edu
Jonathan H. TuPacific Northwest National LaboratoryVerifierad e-postadress på pnnl.gov
Steven CundiffPhysics Department, University of Michigan, Ann Arbor, MI 48109Verifierad e-postadress på umich.edu
Pedro Doria MaiaAssistant Professor of Mathematics, UT ArlingtonVerifierad e-postadress på uta.edu
Niall M ManganAssistant Professor of Engineering Sciences and Applied Math, Northwestern UniversityVerifierad e-postadress på northwestern.edu
Feng LiThe Hong Kong Polytechnic UniversityVerifierad e-postadress på polyu.edu.hk
Travis AskhamAssistant Professor, New Jersey Institute of TechnologyVerifierad e-postadress på njit.edu
Jeffrey A RiffellUniversity of WashingtonVerifierad e-postadress på uw.edu

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J. Nathan Kutz

Professor of Applied Mathematics & Electrical and Computer Engineering

Verifierad e-postadress på uw.edu - Startsida

Dynamical Systems Data Science Machine Learning Optics Neuroscience


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	4561	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	2857	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	2234	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	1862	2016
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017	1610	2017
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018	1368	2018
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016	1134	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	883	2019
Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017	862	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	669	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	641	2016
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017	608	2017
Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013	551	2013
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	528	2016
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021	494	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	466	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	459	2018
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016	434	2016
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	404	2018
Data-driven discovery of Koopman eigenfunctions for control E Kaiser, JN Kutz, SL Brunton Machine Learning: Science and Technology 2 (3), 035023, 2021	401	2021

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