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 | 3966 | 2016 |
Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022 | 2416 | 2022 |
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 | 2046 | 2014 |
Dynamic mode decomposition: data-driven modeling of complex systems JN Kutz, SL Brunton, BW Brunton, JL Proctor Society for Industrial and Applied Mathematics, 2016 | 1659 | 2016 |
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 1440 | 2017 |
Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018 | 1199 | 2018 |
Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016 | 1011 | 2016 |
Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017 | 805 | 2017 |
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 | 756 | 2019 |
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 | 585 | 2018 |
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 | 560 | 2016 |
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017 | 547 | 2017 |
Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013 | 519 | 2013 |
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 | 485 | 2016 |
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 | 410 | 2016 |
Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016 | 404 | 2016 |
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 | 394 | 2018 |
Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021 | 377 | 2021 |
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 | 364 | 2001 |
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 | 354 | 2018 |