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 | 4479 | 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 | 1838 | 2016 |
Data-driven discovery of partial differential equations SH Rudy, SL Brunton, JL Proctor, JN Kutz Science advances 3 (4), e1602614, 2017 | 1581 | 2017 |
Dynamic Mode Decomposition with control JL Proctor, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 15 (1), 142–161, 2016 | 1122 | 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 | 629 | 2016 |
Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature communications 8 (1), 19, 2017 | 596 | 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 | 461 | 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 | 396 | 2018 |
Sparse identification of nonlinear dynamics with control (SINDYc) SL Brunton, JL Proctor, JN Kutz IFAC-PapersOnLine 49 (18), 710-715, 2016 | 347 | 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 | 305 | 2017 |
Discovering dynamic patterns from infectious disease data using dynamic mode decomposition JL Proctor, PA Eckhoff International health 7 (2), 139-145, 2015 | 246 | 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 | 204 | 2015 |
Compressed sensing and dynamic mode decomposition SL Brunton, JL Proctor, JH Tu, JN Kutz Journal of computational dynamics 2 (2), 165-191, 2016 | 178 | 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 | 118 | 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 | 104 | 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 | 101 | 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 | 94 | 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 | 86 | 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 | 81 | 2014 |