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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09317 |
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| _version_ | 1866911202545762304 |
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| author | Brandt, Felix Heuermann, Andreas Hannebohm, Philip Bachmann, Bernhard |
| author_facet | Brandt, Felix Heuermann, Andreas Hannebohm, Philip Bachmann, Bernhard |
| contents | This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid ones. Applied to the large-scale IEEE 14-Bus system, our method achieves a 60% reduction in simulation time compared to conventional simulations, while maintaining the same level of accuracy through error control mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Residual-Informed Learning of Solutions to Algebraic Loops Brandt, Felix Heuermann, Andreas Hannebohm, Philip Bachmann, Bernhard Machine Learning Numerical Analysis 65H10, 37M05 G.1.5; I.2.6 This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid ones. Applied to the large-scale IEEE 14-Bus system, our method achieves a 60% reduction in simulation time compared to conventional simulations, while maintaining the same level of accuracy through error control mechanisms. |
| title | Residual-Informed Learning of Solutions to Algebraic Loops |
| topic | Machine Learning Numerical Analysis 65H10, 37M05 G.1.5; I.2.6 |
| url | https://arxiv.org/abs/2510.09317 |