Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems

• URL: http://arxiv.org/abs/2204.12972v1
• Date: Wed, 27 Apr 2022 14:33:02 GMT
• Title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
• Authors: Oliver Sch\"on, Ricarda-Samantha G\"otte, Julia Timmermann
• Abstract summary: We propose a physics-guided hybrid approach for modeling non-autonomous systems under control. This is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy. Experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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