Koopman operator dynamical models: Learning, analysis and control P Bevanda, S Sosnowski, S Hirche Annual Reviews in Control 52, 197-212, 2021 | 126 | 2021 |
Diffeomorphically Learning Stable Koopman Operators P Bevanda, M Beier, S Kerz, A Lederer, S Sosnowski, S Hirche IEEE Control Systems Letters 6, 3427-3432, 2022 | 30* | 2022 |
Koopman Kernel Regression P Bevanda, M Beier, A Lederer, S Sosnowski, E Hüllermeier, S Hirche Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
Learning the Koopman eigendecomposition: A diffeomorphic approach P Bevanda, J Kirmayr, S Sosnowski, S Hirche 2022 American Control Conference (ACC), 2736-2741, 2022 | 11 | 2022 |
Towards Data-driven LQR with Koopmanizing Flows P Bevanda, M Beier, S Heshmati-Alamdari, S Sosnowski, S Hirche IFAC Conference on Intelligent Control and Automation Sciences (ICONS) 2022 …, 2022 | 9 | 2022 |
Nonparametric Control-Koopman Operator Learning: Flexible and Scalable Models for Prediction and Control P Bevanda, B Driessen, LC Iacob, R Toth, S Sosnowski, S Hirche arXiv preprint arXiv:2405.07312, 2024 | 2 | 2024 |
Data-Driven Optimal Feedback Laws via Kernel Mean Embeddings P Bevanda, N Hoischen, S Sosnowski, S Hirche, B Houska arXiv preprint arXiv:2407.16407, 2024 | | 2024 |
Gaussian Process-Based Representation Learning via Timeseries Symmetries P Bevanda, M Beier, A Lederer, A Capone, SG Sosnowski, S Hirche ICML 2024 Workshop on Geometry-grounded Representation Learning and …, 0 | | |