| 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 | 3763 | 2016 |
| Dynamic mode decomposition: data-driven modeling of complex systems JN Kutz, SL Brunton, BW Brunton, JL Proctor Society for Industrial and Applied Mathematics, 2016 | 1597 | 2016 |
| Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 1384 | 2017 |
| Dynamic Mode Decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142–161, 2016 | 963 | 2016 |
| Dynamic mode decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142-161, 2016 | 963 | 2016 |
| 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 | 544 | 2016 |
| Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017 | 530 | 2017 |
| 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 | 392 | 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 | 338 | 2018 |
| Sparse identification of nonlinear dynamics with control (SINDYc) SL Brunton, JL Proctor, JN Kutz IFAC-PapersOnLine 49 (18), 710-715, 2016 | 292 | 2016 |
| Model selection for dynamical systems via sparse regression and information criteria NM Mangan, JN Kutz, SL Brunton, JL Proctor Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 278 | 2017 |
| Discovering dynamic patterns from infectious disease data using dynamic mode decomposition JL Proctor, PA Eckhoff International health 7 (2), 139-145, 2015 | 224 | 2015 |
| Modeling malaria genomics reveals transmission decline and rebound in Senegal RF Daniels, SF Schaffner, EA Wenger, JL Proctor, HH Chang, W Wong, ... Proceedings of the National Academy of Sciences 112 (22), 7067-7072, 2015 | 188 | 2015 |
| Compressed sensing and dynamic mode decomposition SL Brunton, JL Proctor, JH Tu, JN Kutz Journal of computational dynamics 2 (2), 165-191, 2016 | 161 | 2016 |
| Model selection for hybrid dynamical systems via sparse regression NM Mangan, T Askham, SL Brunton, JN Kutz, JL Proctor Proceedings of the Royal Society A 475 (2223), 20180534, 2019 | 102 | 2019 |
| Passive mode-locking by use of waveguide arrays JL Proctor, JN Kutz Optics letters 30 (15), 2013-2015, 2005 | 94 | 2005 |
| Dynamic mode decomposition for compressive system identification Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton AIAA Journal 58 (2), 561-574, 2020 | 91 | 2020 |
| Sparse sensor placement optimization for classification BW Brunton, SL Brunton, JL Proctor, JN Kutz SIAM Journal on Applied Mathematics 76 (5), 2099-2122, 2016 | 90 | 2016 |
| Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems J Nathan Kutz, JL Proctor, SL Brunton Complexity 2018, 1-16, 2018 | 84 | 2018 |
| Nonlinear mode-coupling for passive mode-locking: application of waveguide arrays, dual-core fibers, and/or fiber arrays J Proctor, JN Kutz Optics express 13 (22), 8933-8950, 2005 | 80 | 2005 |
