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Eurika Kaiser
Eurika Kaiser
Verified email at uw.edu
Title
Cited by
Cited by
Year
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
3272017
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
2782018
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
2152014
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, S Brunton
Machine Learning: Science and Technology, 2021
1732021
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
602020
Modern Koopman Theory for Dynamical Systems
SL Brunton, M Budišić, E Kaiser, JN Kutz
arXiv preprint arXiv:2102.12086, 2021
502021
Dynamic mode decomposition for compressive system identification
Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton
Bulletin of the American Physical Society, 2017
492017
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
462016
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
452015
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
442019
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
372017
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
372017
Discovering conservation laws from data for control
E Kaiser, JN Kutz, SL Brunton
2018 IEEE Conference on Decision and Control (CDC), 6415-6421, 2018
312018
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 (11), 1-8, 2014
312014
Data-driven approximations of dynamical systems operators for control
E Kaiser, JN Kutz, SL Brunton
The Koopman Operator in Systems and Control, 197-234, 2020
282020
Optimized sampling for multiscale dynamics
K Manohar, E Kaiser, SL Brunton, JN Kutz
Multiscale Modeling & Simulation 17 (1), 117-136, 2019
272019
Learning Discrepancy Models From Experimental Data
K Kaheman, E Kaiser, B Strom, JN Kutz, SL Brunton
arXiv preprint arXiv:1909.08574, 2019
222019
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
222019
Sparsity enabled cluster reduced-order models for control
E Kaiser, M Morzyński, G Daviller, JN Kutz, BW Brunton, SL Brunton
Journal of Computational Physics, 2017
212017
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
152020
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