{"type":"conference","page":"19-24","publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","issue":"12","date_created":"2022-05-05T06:22:55Z","status":"public","author":[{"first_name":"Oliver","last_name":"Schön","full_name":"Schön, Oliver"},{"full_name":"Götte, Ricarda-Samantha","last_name":"Götte","first_name":"Ricarda-Samantha","id":"43992"},{"full_name":"Timmermann, Julia","last_name":"Timmermann","id":"15402","first_name":"Julia"}],"intvolume":"        55","_id":"31066","conference":{"name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","end_date":"2022-07-01","start_date":"2022-06-29","location":"Casablanca, Morocco"},"year":"2022","volume":55,"doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","abstract":[{"lang":"eng","text":"While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. "}],"keyword":["neural networks","physics-guided","data-driven","multi-objective optimization","system identification","machine learning","dynamical systems"],"title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","date_updated":"2023-05-02T15:11:20Z","language":[{"iso":"eng"}],"quality_controlled":"1","department":[{"_id":"153"}],"citation":{"mla":"Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, vol. 55, no. 12, 2022, pp. 19–24, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","ieee":"O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","apa":"Schön, O., Götte, R.-S., &#38; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, <i>55</i>(12), 19–24. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","short":"O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.","ama":"Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>. Vol 55. ; 2022:19-24. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","bibtex":"@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>}, number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022}, pages={19–24} }","chicago":"Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” In <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, 55:19–24, 2022. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>."},"user_id":"43992"}