Követés
J. Nathan Kutz
J. Nathan Kutz
Professor of Applied Mathematics & Electrical and Computer Engineering
E-mail megerősítve itt: uw.edu - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
35852016
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
19362014
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2019
17152019
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
15352016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
13512017
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
10862018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
9242016
Deep learning in fluid dynamics
JN Kutz
Journal of Fluid Mechanics 814, 1-4, 2017
7662017
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
6852019
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
5312016
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
5302018
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
5132017
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2022
5112022
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
OUP Oxford, 2013
4962013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
OUP Oxford, 2013
4962013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
OUP Oxford, 2013
4962013
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
4572016
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
3852016
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
3802016
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
3722018
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Cikkek 1–20