| 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 | 4561 | 2016 |
| Data-driven science and engineering: Machine learning, dynamical systems, and control SL Brunton, JN Kutz Cambridge University Press, 2022 | 2857 | 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 | 2234 | 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 | 1862 | 2016 |
| Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 1610 | 2017 |
| Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018 | 1368 | 2018 |
| Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016 | 1134 | 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 | 883 | 2019 |
| Deep learning in fluid dynamics JN Kutz Journal of Fluid Mechanics 814, 1-4, 2017 | 862 | 2017 |
| 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 | 669 | 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 | 641 | 2016 |
| Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017 | 608 | 2017 |
| Data-driven modeling & scientific computation: methods for complex systems & big data JN Kutz OUP Oxford, 2013 | 551 | 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 | 528 | 2016 |
| Modern Koopman theory for dynamical systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021 | 494 | 2021 |
| 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 | 466 | 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 | 459 | 2018 |
| Multiresolution dynamic mode decomposition JN Kutz, X Fu, SL Brunton SIAM Journal on Applied Dynamical Systems 15 (2), 713-735, 2016 | 434 | 2016 |
| 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 | 404 | 2018 |
| Data-driven discovery of Koopman eigenfunctions for control E Kaiser, JN Kutz, SL Brunton Machine Learning: Science and Technology 2 (3), 035023, 2021 | 401 | 2021 |
Steven L. BruntonProfessor, University of Washington在 uw.edu 的电子邮件经过验证