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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
39632016
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2022
24132022
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
20442014
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
16592016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
14402017
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
11982018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
10092016
Deep learning in fluid dynamics
JN Kutz
Journal of Fluid Mechanics 814, 1-4, 2017
8052017
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
7552019
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
5852018
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
5602016
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
5472017
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
OUP Oxford, 2013
5192013
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
4852016
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
4102016
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
4042016
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
3942018
Modern Koopman theory for dynamical systems
SL Brunton, M Budišić, E Kaiser, JN Kutz
arXiv preprint arXiv:2102.12086, 2021
3772021
Bose-Einstein condensates in standing waves: The cubic nonlinear Schrödinger equation with a periodic potential
JC Bronski, LD Carr, B Deconinck, JN Kutz
Physical Review Letters 86 (8), 1402, 2001
3652001
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
3532018
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