Orthogonal projection-based regularization for efficient model augmentation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913939472777216 |
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| author | Györök, Bendegúz M. Hoekstra, Jan H. Kon, Johan Péni, Tamás Schoukens, Maarten Tóth, Roland |
| author_facet | Györök, Bendegúz M. Hoekstra, Jan H. Kon, Johan Péni, Tamás Schoukens, Maarten Tóth, Roland |
| contents | Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and a considerable part of the learning effort is often spent on capturing already expected/known behavior of the system, that can be accurately described by first-principles laws of physics. A potential solution is to directly integrate such prior physical knowledge into the model structure, combining the strengths of physics-based modeling and deep-learning-based identification. The most common approach is to use an additive model augmentation structure, where the physics-based and the machine-learning (ML) components are connected in parallel, i.e., additively. However, such models are overparametrized, training them is challenging, potentially causing the physics-based part to lose interpretability. To overcome this challenge, this paper proposes an orthogonal projection-based regularization technique to enhance parameter learning and even model accuracy in learning-based augmentation of nonlinear baseline models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Orthogonal projection-based regularization for efficient model augmentation Györök, Bendegúz M. Hoekstra, Jan H. Kon, Johan Péni, Tamás Schoukens, Maarten Tóth, Roland Machine Learning Systems and Control Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and a considerable part of the learning effort is often spent on capturing already expected/known behavior of the system, that can be accurately described by first-principles laws of physics. A potential solution is to directly integrate such prior physical knowledge into the model structure, combining the strengths of physics-based modeling and deep-learning-based identification. The most common approach is to use an additive model augmentation structure, where the physics-based and the machine-learning (ML) components are connected in parallel, i.e., additively. However, such models are overparametrized, training them is challenging, potentially causing the physics-based part to lose interpretability. To overcome this challenge, this paper proposes an orthogonal projection-based regularization technique to enhance parameter learning and even model accuracy in learning-based augmentation of nonlinear baseline models. |
| title | Orthogonal projection-based regularization for efficient model augmentation |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2501.05842 |