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arXiv · 2601.18300

Gradient-Informed Machine Learning in Electromagnetics

Matteo Zorzetto·Merle Backmeyer·Michael Wiesheu·Riccardo Torchio·Fabrizio Dughiero·Sebastian Schöps
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Abstract

Simulation techniques such as the finite element method are essential for designing electrical devices, but their computational cost can be prohibitive for repeated or real-time computations. Projection-based model order reduction techniques mitigate this by reducing the model size and complexity, yet face challenges when extended to nonlinear or non-affine parametric models. In this work, Isogeometric Analysis (IGA) is combined with proper orthogonal decomposition and Gaussian process regression to construct a non-intrusive surrogate model of a parametric nonlinear model of a permanent magnet synchronous machine. The differentiable nature of IGA allows for computationally efficient extraction of parametric sensitivities, which are leveraged for gradient-enhanced surrogate modeling.

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Matteo Zorzetto, Merle Backmeyer, Michael Wiesheu, Riccardo Torchio, Fabrizio Dughiero, Sebastian Schöps. 2026-01-26. Gradient-Informed Machine Learning in Electromagnetics. https://doi.org/10.1109/tmag.2026.3702291

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