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Eurika Kaiser
Eurika Kaiser
University of Washington
Bestätigte E-Mail-Adresse bei uw.edu
Titel
Zitiert von
Zitiert von
Jahr
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
6772018
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
6142017
Modern Koopman Theory for Dynamical Systems
SL Brunton, M Budišić, E Kaiser, JN Kutz
arXiv preprint arXiv:2102.12086, 2021
5072021
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, SL Brunton
Machine Learning: Science and Technology 2 (3), 035023, 2021
4082021
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
2972014
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
1762020
Dynamic mode decomposition for compressive system identification
Z Bai, E Kaiser, JL Proctor, JN Kutz, SL Brunton
Bulletin of the American Physical Society, 2017
1092017
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
952016
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
852021
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
802019
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
732019
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
672020
Learning Discrepancy Models From Experimental Data
K Kaheman, E Kaiser, B Strom, JN Kutz, SL Brunton
arXiv preprint arXiv:1909.08574, 2019
662019
Discovering conservation laws from data for control
E Kaiser, JN Kutz, SL Brunton
2018 IEEE Conference on Decision and Control (CDC), 6415-6421, 2018
652018
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
562017
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
562017
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
552015
Optimized sampling for multiscale dynamics
K Manohar, E Kaiser, SL Brunton, JN Kutz
Multiscale Modeling & Simulation 17 (1), 117-136, 2019
512019
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
362014
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
342020
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