Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks

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Abstract

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-off hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example.
Original languageEnglish
Pages (from-to)295-300
Number of pages6
JournalIFAC-PapersOnLine
Volume58
Issue number15
DOIs
Publication statusPublished - 2024
Event20th IFAC Symposium on System Identification, SYSID 2024 - Boston, United States
Duration: 17 Jul 202419 Jul 2024
Conference number: 20

Funding

This work is funded by the European Union (ERC, COMPLETE, 101075836). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Keywords

  • System Identification
  • Physics-Guided Neural Networks,
  • State Space
  • Physics-Guided Neural Networks

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