| Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems JN Kutz, SL Brunton, BW Brunton, JL Proctor Society for Industrial and Applied Mathematics, 2016 | 1379 | 2016 |
| Rats and Humans Can Optimally Accumulate Evidence for Decision-Making BW Brunton, MM Botvinick, CD Brody Science 340 (6128), 95-98, 2013 | 607 | 2013 |
| 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 | 473 | 2016 |
| Distinct relationships of parietal and prefrontal cortices to evidence accumulation TD Hanks, CD Kopec, BW Brunton, CA Duan, JC Erlich, CD Brody Nature 520 (7546), 220-223, 2015 | 467 | 2015 |
| Chaos as an intermittently forced linear system SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz Nature Communications 8, 2017 | 466 | 2017 |
| 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 | 427 | 2016 |
| Cell shape and cell-wall organization in Gram-negative bacteria KC Huang, R Mukhopadhyay, B Wen, Z Gitai, NS Wingreen Proceedings of the National Academy of Sciences 105 (49), 19282-19287, 2008 | 368 | 2008 |
| 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 38 (3), 63-86, 2018 | 325 | 2018 |
| Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat JC Erlich, BW Brunton, CA Duan, TD Hanks, CD Brody Elife 4, e05457, 2015 | 209 | 2015 |
| Anipose: a toolkit for robust markerless 3D pose estimation P Karashchuk, KL Rupp, ES Dickinson, S Walling-Bell, E Sanders, E Azim, ... Cell Reports 36 (13), 109730, 2021 | 136 | 2021 |
| Sparse Sensor Placement Optimization for Classification BW Brunton, SL Brunton, JL Proctor, JN Kutz SIAM Journal on Applied Mathematics 76 (5), 2099-2122, 2016 | 105* | 2016 |
| Cortical and Subcortical Contributions to Short-Term Memory for Orienting Movements CD Kopec, JC Erlich, BW Brunton, K Deisseroth, CD Brody Neuron 88 (2), 367-377, 2015 | 105 | 2015 |
| Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control U Fasel, JN Kutz, BW Brunton, SL Brunton Proceedings of the Royal Society A 478 (2260), 20210904, 2022 | 96 | 2022 |
| 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 | 72 | 2014 |
| Numerical differentiation of noisy data: A unifying multi-objective optimization framework F van Breugel, JN Kutz, BW Brunton IEEE Access 8, 196865 - 196877, 2020 | 58 | 2020 |
| Learning dominant physical processes with data-driven balance models JL Callaham, JV Koch, BW Brunton, JN Kutz, SL Brunton Nature Communications 12 (1), 1-10, 2021 | 55 | 2021 |
| Data-driven methods in fluid dynamics: Sparse classification from experimental data Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack Whither Turbulence and Big Data in the 21st Century?, 323-342, 2017 | 51 | 2017 |
| Data-Driven Methods in Fluid Dynamics: Sparse Classification from Experimental Data Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack Whither Turbulence and Big Data in the 21st Century?, 323-342, 2017 | 51 | 2017 |
| Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data TL Mohren, TL Daniel, SL Brunton, BW Brunton Proceedings of the National Academy of Science 115 (42), 10564--10569, 2018 | 46 | 2018 |
| AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video XRN Wang, A Farhadi, R Rao, B Brunton AAAI Conference on Artificial Intelligence, 2524, 2018 | 43 | 2018 |
J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringVerified email at uw.edu
