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
Edwin DingAzusa Pacific UniversityPatvirtintas el. paštas apu.edu
Bethany LuschArgonne National LabPatvirtintas el. paštas anl.gov
Jared BronskiUniversity of IllinoisPatvirtintas el. paštas math.uiuc.edu
Jonathan H. TuPacific Northwest National LaboratoryPatvirtintas el. paštas pnnl.gov
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

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	4600	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	2904	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	2243	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
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	889	2019
Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017	869	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	673	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
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017	611	2017
Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013	554	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	529	2016
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	468	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	462	2018
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016	438	2016
Data-driven discovery of Koopman eigenfunctions for control E Kaiser, JN Kutz, SL Brunton Machine Learning: Science and Technology 2 (3), 035023, 2021	407	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	405	2018

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