| 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 | 4384 | 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 | 1800 | 2016 |
| Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 1552 | 2017 |
| Dynamic Mode Decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142–161, 2016 | 1100 | 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 | 618 | 2016 |
| Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017 | 588 | 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 | 454 | 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 | 388 | 2018 |
| Sparse identification of nonlinear dynamics with control (SINDYc) SL Brunton, JL Proctor, JN Kutz IFAC-PapersOnLine 49 (18), 710-715, 2016 | 341 | 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 | 299 | 2017 |
| Discovering dynamic patterns from infectious disease data using dynamic mode decomposition JL Proctor, PA Eckhoff International health 7 (2), 139-145, 2015 | 240 | 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 | 201 | 2015 |
| Compressed sensing and dynamic mode decomposition SL Brunton, JL Proctor, JH Tu, JN Kutz Journal of computational dynamics 2 (2), 165-191, 2016 | 173 | 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 | 117 | 2019 |
| Dynamic mode decomposition for compressive system identification Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton AIAA Journal 58 (2), 561-574, 2020 | 99 | 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 | 99 | 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), 6010634, 2018 | 93 | 2018 |
| Passive mode-locking by use of waveguide arrays JL Proctor, JN Kutz Optics letters 30 (15), 2013-2015, 2005 | 93 | 2005 |
| The seasonality of diarrheal pathogens: A retrospective study of seven sites over three years DL Chao, A Roose, M Roh, KL Kotloff, JL Proctor PLoS neglected tropical diseases 13 (8), e0007211, 2019 | 83 | 2019 |
| Exploiting sparsity and equation-free architectures in complex systems JL Proctor, SL Brunton, BW Brunton, JN Kutz The European Physical Journal Special Topics 223 (13), 2665-2684, 2014 | 80 | 2014 |
