TY - GEN
T1 - AutoKoopman
T2 - 21st International Symposium on Automated Technology for Verification and Analysis, ATVA 2023
AU - Lew, Ethan
AU - Hekal, Abdelrahman
AU - Potomkin, Kostiantyn
AU - Kochdumper, Niklas
AU - Hencey, Brandon
AU - Bak, Stanley
AU - Bogomolov, Sergiy
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - While Koopman operator linearization has brought many advances for prediction, control, and verification of dynamical systems, its main disadvantage is that the quality of the resulting model heavily depends on the correct tuning of hyper-parameters such as the number of observables. Our toolbox is a Python package that automates learning accurate models in a Koopman linearized representation with low effort, offering several tuning strategies to optimize the hyper-parameters associated with the Koopman operator techniques automatically. supports discrete as well as continuous-time models and implements all major types of observables, which are polynomials, random Fourier features, and neural networks. As we demonstrate on several benchmarks, our toolbox is able to automatically identify very accurate dynamic models for symbolic, black-box, as well as real systems. AutoKoopman is available at https://github.com/EthanJamesLew/AutoKoopman and on PyPI as.
AB - While Koopman operator linearization has brought many advances for prediction, control, and verification of dynamical systems, its main disadvantage is that the quality of the resulting model heavily depends on the correct tuning of hyper-parameters such as the number of observables. Our toolbox is a Python package that automates learning accurate models in a Koopman linearized representation with low effort, offering several tuning strategies to optimize the hyper-parameters associated with the Koopman operator techniques automatically. supports discrete as well as continuous-time models and implements all major types of observables, which are polynomials, random Fourier features, and neural networks. As we demonstrate on several benchmarks, our toolbox is able to automatically identify very accurate dynamic models for symbolic, black-box, as well as real systems. AutoKoopman is available at https://github.com/EthanJamesLew/AutoKoopman and on PyPI as.
KW - deep Koopman
KW - Koopman operator linearization
KW - random Fourier features
KW - system identification
UR - https://www.scopus.com/pages/publications/85149849023
U2 - 10.1007/978-3-031-45332-8_12
DO - 10.1007/978-3-031-45332-8_12
M3 - Conference contribution
AN - SCOPUS:85149849023
SN - 9783031453311
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 237
EP - 250
BT - Automated Technology for Verification and Analysis - 21st International Symposium, ATVA 2023, Proceedings
A2 - André, Étienne
A2 - Sun, Jun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 October 2023 through 27 October 2023
ER -