J. Nathan Kutz

Cited by

	All	Since 2019
Citations	41039	33733
h-index	88	74
i10-index	304	236

9000

4500

2250

6750

20022003200420052006200720082009201020112012201320142015201620172018201920202021202220232024129 114 148 204 160 196 301 298 325 366 287 284 311 394 687 968 1452 2187 3459 5300 6467 8508 7527

Public access

View all

173 articles

12 articles

available

not available

Based on funding mandates

Co-authors

Steven L. BruntonProfessor, University of WashingtonVerified email at uw.edu
Joshua L. ProctorPrincipal Research Scientist, Bill and Melinda Gates FoundationVerified email at idmod.org
Bingni W. BruntonProfessor, Richard and Joan Komen University Chair, University of Washington, SeattleVerified email at uw.edu
Eurika KaiserUniversity of WashingtonVerified email at uw.edu
Krithika ManoharUniversity of WashingtonVerified email at uw.edu
N. Benjamin ErichsonAssistant ProfessorVerified email at pitt.edu
Xing FuAnt GroupVerified email at antgroup.com
Keren BergmanColumbia UniversityVerified email at ee.columbia.edu
PKA WaiThe Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
Matthew WilliamsOceanitVerified email at oceanit.com
Edwin DingAzusa Pacific UniversityVerified email at apu.edu
Bethany LuschArgonne National LabVerified email at anl.gov
Jared BronskiUniversity of IllinoisVerified email at math.uiuc.edu
Jonathan H. TuPacific Northwest National LaboratoryVerified email at pnnl.gov
Steven CundiffPhysics Department, University of Michigan, Ann Arbor, MI 48109Verified email at umich.edu
Pedro Doria MaiaAssistant Professor of Mathematics, UT ArlingtonVerified email at uta.edu
Niall M ManganAssistant Professor of Engineering Sciences and Applied Math, Northwestern UniversityVerified email at northwestern.edu
Feng LiThe Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
Travis AskhamAssistant Professor, New Jersey Institute of TechnologyVerified email at njit.edu
Jeffrey A RiffellUniversity of WashingtonVerified email at uw.edu

J. Nathan Kutz

Professor of Applied Mathematics & Electrical and Computer Engineering

Verified email at uw.edu - Homepage

Dynamical Systems Data Science Machine Learning Optics Neuroscience


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	4405	2016
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022	2722	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	2184	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	1810	2016
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017	1562	2017
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018	1327	2018
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016	1109	2016
Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017	846	2017
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	839	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	637	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	621	2016
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017	590	2017
Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013	541	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	517	2016
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021	465	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	457	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	439	2018
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016	431	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	391	2018
Data-driven discovery of Koopman eigenfunctions for control E Kaiser, JN Kutz, SL Brunton Machine Learning: Science and Technology 2 (3), 035023, 2021	388	2021

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors