{"abstract":[{"lang":"eng","text":"While trade-offs between modeling effort and model accuracy remain a major\nconcern with system identification, resorting to data-driven methods often\nleads to a complete disregard for physical plausibility. To address this issue,\nwe propose a physics-guided hybrid approach for modeling non-autonomous systems\nunder control. Starting from a traditional physics-based model, this is\nextended by a recurrent neural network and trained using a sophisticated\nmulti-objective strategy yielding physically plausible models. While purely\ndata-driven methods fail to produce satisfying results, experiments conducted\non real data reveal substantial accuracy improvements by our approach compared\nto a physics-based model."}],"author":[{"full_name":"Schön, Oliver","last_name":"Schön","first_name":"Oliver"},{"first_name":"Ricarda-Samantha","last_name":"Götte","full_name":"Götte, Ricarda-Samantha"},{"last_name":"Timmermann","full_name":"Timmermann, Julia","first_name":"Julia"}],"year":"2022","status":"public","date_created":"2022-12-18T10:49:28Z","citation":{"ieee":"O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying   Non-Autonomous Dynamical Systems,” <i>arXiv:2204.12972</i>. 2022, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">10.1016/j.ifacol.2022.07.282</a>.","ama":"Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying   Non-Autonomous Dynamical Systems. <i>arXiv:220412972</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">10.1016/j.ifacol.2022.07.282</a>","mla":"Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying  Non-Autonomous Dynamical Systems.” <i>ArXiv:2204.12972</i>, 2022, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">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. In <i>arXiv:2204.12972</i>. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","bibtex":"@article{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying   Non-Autonomous Dynamical Systems}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">10.1016/j.ifacol.2022.07.282</a>}, journal={arXiv:2204.12972}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022} }","chicago":"Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying  Non-Autonomous Dynamical Systems.” <i>ArXiv:2204.12972</i>, 2022. <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, ArXiv:2204.12972 (2022)."},"user_id":"55553","external_id":{"arxiv":["2204.12972"]},"title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying\n  Non-Autonomous Dynamical Systems","_id":"34524","publication":"arXiv:2204.12972","date_updated":"2022-12-18T10:56:39Z","type":"preprint","doi":"10.1016/j.ifacol.2022.07.282"}