Learning electromagnetic fields based on finite element basis functions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914241967030272 |
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| author | Backmeyer, Merle Wiesheu, Michael Schöps, Sebastian |
| author_facet | Backmeyer, Merle Wiesheu, Michael Schöps, Sebastian |
| contents | Parametric surrogate models of electric machines are widely used for efficient design optimization and operational monitoring. Addressing geometry variations, spline-based computer-aided design representations play a pivotal role. In this study, we propose a novel approach that combines isogeometric analysis, proper orthogonal decomposition and deep learning to enable rapid and physically consistent predictions by directly learning spline basis coefficients. The effectiveness of this method is demonstrated using a parametric nonlinear magnetostatic model of a permanent magnet synchronous machine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19255 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning electromagnetic fields based on finite element basis functions Backmeyer, Merle Wiesheu, Michael Schöps, Sebastian Computational Engineering, Finance, and Science Computational Physics Parametric surrogate models of electric machines are widely used for efficient design optimization and operational monitoring. Addressing geometry variations, spline-based computer-aided design representations play a pivotal role. In this study, we propose a novel approach that combines isogeometric analysis, proper orthogonal decomposition and deep learning to enable rapid and physically consistent predictions by directly learning spline basis coefficients. The effectiveness of this method is demonstrated using a parametric nonlinear magnetostatic model of a permanent magnet synchronous machine. |
| title | Learning electromagnetic fields based on finite element basis functions |
| topic | Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2507.19255 |