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J. Nathan Kutz
J. Nathan Kutz
Professor of Applied Mathematics & Electrical and Computer Engineering
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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
27602016
Dynamic mode decomposition: Theory and applications
JH Tu
Princeton University, 2013
16532013
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2022
14322022
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
12312016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
11162017
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
8432018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
7432016
Deep learning in fluid dynamics
JN Kutz
Journal of Fluid Mechanics 814, 1-4, 2017
6492017
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
4972019
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
4322016
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
4302013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
4302013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
4302013
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
4232017
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
3942016
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
3902018
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
3522001
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
3272016
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
3122016
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
2782018
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Artikelen 1–20