J. Nathan Kutz
J. Nathan Kutz
Professor of Applied Mathematics
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
16102016
Dynamic mode decomposition: Theory and applications
JH Tu
Princeton University, 2013
11432013
Dynamic mode decomposition: data-driven modeling of complex systems
JN Kutz, SL Brunton, BW Brunton, JL Proctor
Society for Industrial and Applied Mathematics, 2016
7722016
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science Advances 3 (4), e1602614, 2017
6992017
Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz
Cambridge University Press, 2019
6222019
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 1-10, 2018
4832018
Dynamic mode decomposition with control
JL Proctor, SL Brunton, JN Kutz
SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016
4792016
Deep learning in fluid dynamics
JN Kutz
Journal of Fluid Mechanics 814, 1-4, 2017
4252017
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
3332001
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
3232013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
3232013
Data-driven modeling & scientific computation: methods for complex systems & big data
JN Kutz
Oxford University Press, 2013
3232013
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
3052016
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
2802016
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 1-9, 2017
2662017
Multiresolution dynamic mode decomposition
JN Kutz, X Fu, SL Brunton
SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016
2402016
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
2292019
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
2282018
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
2092016
Mode-locked soliton lasers
JN Kutz
SIAM review 48 (4), 629-678, 2006
2082006
The system can't perform the operation now. Try again later.
Articles 1–20