--- _id: '31066' 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. ' author: - first_name: Oliver full_name: Schön, Oliver last_name: Schön - first_name: Ricarda-Samantha full_name: Götte, Ricarda-Samantha id: '43992' last_name: Götte - first_name: Julia full_name: Timmermann, Julia id: '15402' last_name: Timmermann citation: ama: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55. ; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282' apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12), 19–24. https://doi.org/10.1016/j.ifacol.2022.07.282 bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55}, DOI={https://doi.org/10.1016/j.ifacol.2022.07.282}, 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 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282. ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.' mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24, doi:https://doi.org/10.1016/j.ifacol.2022.07.282. short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.' conference: end_date: 2022-07-01 location: Casablanca, Morocco name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022) start_date: 2022-06-29 date_created: 2022-05-05T06:22:55Z date_updated: 2023-05-02T15:11:20Z department: - _id: '153' doi: https://doi.org/10.1016/j.ifacol.2022.07.282 intvolume: ' 55' issue: '12' keyword: - neural networks - physics-guided - data-driven - multi-objective optimization - system identification - machine learning - dynamical systems language: - iso: eng page: 19-24 publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022) quality_controlled: '1' status: public title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems type: conference user_id: '43992' volume: 55 year: '2022' ...