| 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 |
| 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 |
| Modern Koopman Theory for Dynamical Systems SL Brunton, M Budišić, E Kaiser, JN Kutz arXiv preprint arXiv:2102.12086, 2021 | 494 | 2021 |
| 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 |
| Cluster-based reduced-order modelling of a mixing layer E Kaiser, BR Noack, L Cordier, A Spohn, M Segond, M Abel, G Daviller, ... Journal of Fluid Mechanics 754, 365-414, 2014 | 296 | 2014 |
| Time-delay observables for koopman: Theory and applications M Kamb, E Kaiser, SL Brunton, JN Kutz SIAM Journal on Applied Dynamical Systems 19 (2), 886-917, 2020 | 170 | 2020 |
| Dynamic mode decomposition for compressive system identification Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton Bulletin of the American Physical Society, 2017 | 109 | 2017 |
| Drag reduction of a car model by linear genetic programming control R Li, BR Noack, L Cordier, J Borée, F Harambat, E Kaiser, T Duriez arXiv preprint arXiv:1609.02505, 2016 | 91 | 2016 |
| Sindy with control: A tutorial U Fasel, E Kaiser, JN Kutz, BW Brunton, SL Brunton 2021 60th IEEE Conference on Decision and Control (CDC), 16-21, 2021 | 83 | 2021 |
| Cluster-based feedback control of turbulent post-stall separated flows AG Nair, CA Yeh, E Kaiser, BR Noack, SL Brunton, K Taira Journal of Fluid Mechanics 875, 345-375, 2019 | 79 | 2019 |
| Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems S Li, E Kaiser, S Laima, H Li, SL Brunton, JN Kutz Physical Review E 100 (2), 022220, 2019 | 73 | 2019 |
| Discovering conservation laws from data for control E Kaiser, JN Kutz, SL Brunton 2018 IEEE Conference on Decision and Control (CDC), 6415-6421, 2018 | 66 | 2018 |
| Data-driven approximations of dynamical systems operators for control E Kaiser, JN Kutz, SL Brunton The Koopman Operator in Systems and Control: Concepts, Methodologies, and …, 2020 | 65 | 2020 |
| Learning Discrepancy Models From Experimental Data K Kaheman, E Kaiser, B Strom, JN Kutz, SL Brunton arXiv preprint arXiv:1909.08574, 2019 | 65 | 2019 |
| 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 | 56 | 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 | 56 | 2017 |
| Cluster-based reduced-order modelling of the flow in the wake of a high speed train J Östh, E Kaiser, S Krajnović, BR Noack Journal of Wind Engineering and Industrial Aerodynamics 145, 327-338, 2015 | 54 | 2015 |
| Optimized sampling for multiscale dynamics K Manohar, E Kaiser, SL Brunton, JN Kutz Multiscale Modeling & Simulation 17 (1), 117-136, 2019 | 51 | 2019 |
| Cluster-based analysis of cycle-to-cycle variations: application to internal combustion engines Y Cao, E Kaiser, J Borée, BR Noack, L Thomas, S Guilain Experiments in fluids 55, 1-8, 2014 | 36 | 2014 |
| Deep reinforcement learning for optical systems: A case study of mode-locked lasers C Sun, E Kaiser, SL Brunton, JN Kutz Machine Learning: Science and Technology 1 (4), 045013, 2020 | 34 | 2020 |
Steven L. BruntonProfessor, University of Washington在 uw.edu 的电子邮件经过验证