| 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 | 6247 | 2016 |
| Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022 | 4029 | 2022 |
| Machine learning for fluid mechanics SL Brunton, BR Noack, P Koumoutsakos Annual review of fluid mechanics 52 (1), 477-508, 2020 | 3502 | 2020 |
| On dynamic mode decomposition: Theory and applications JH Tu, CW Rowley, DM Luchtenburg, SL Brunton, JN Kutz Journal of Computational Dynamics 1 (2), 391-421, 2014 | 2745 | 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 | 2351 | 2016 |
| Modal analysis of fluid flows: An overview K Taira, SL Brunton, STM Dawson, CW Rowley, T Colonius, BJ McKeon, ... AIAA journal 55 (12), 4013-4041, 2017 | 2285 | 2017 |
| Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 2063 | 2017 |
| Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018 | 1860 | 2018 |
| Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016 | 1463 | 2016 |
| 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 | 1237 | 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 | 882 | 2018 |
| Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021 | 830 | 2021 |
| 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 | 826 | 2016 |
| Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature Communications 8 (19), 1--9, 2017 | 765 | 2017 |
| Closed-loop turbulence control: Progress and challenges SL Brunton, BR Noack Applied Mechanics Reviews 67 (5), 050801, 2015 | 738 | 2015 |
| Modal analysis of fluid flows: Applications and outlook K Taira, MS Hemati, SL Brunton, Y Sun, K Duraisamy, S Bagheri, ... AIAA journal 58 (3), 998-1022, 2020 | 702 | 2020 |
| Enhancing computational fluid dynamics with machine learning R Vinuesa, SL Brunton Nature Computational Science 2 (6), 358-366, 2022 | 669 | 2022 |
| 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 | 615 | 2018 |
| 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 …, 2017 | 562 | 2017 |
| Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016 | 539 | 2016 |
J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringVerified email at uw.edu
