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J. Nathan Kutz
J. Nathan Kutz
Professor of Applied Mathematics & Electrical and Computer Engineering
Verified email at uw.edu - Homepage
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
47842016
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2022
30492022
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
23082014
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
19332016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
16702017
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
14402018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
11802016
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
9272019
Deep learning in fluid dynamics
JN Kutz
Journal of Fluid Mechanics 814, 1-4, 2017
8902017
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
7062018
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
6642016
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
6362017
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
OUP Oxford, 2013
5622013
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
5412016
Modern Koopman theory for dynamical systems
SL Brunton, M Budišić, E Kaiser, JN Kutz
arXiv preprint arXiv:2102.12086, 2021
5352021
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
4852018
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
4842016
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
4552016
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, SL Brunton
Machine Learning: Science and Technology 2 (3), 035023, 2021
4192021
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
4172018
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