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arXiv · 2510.14487

A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes

Riccardo Basei·Francesco Pase·Francesco Lucchini·Francesco Toso·Riccardo Torchio
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Abstract

Efficient modeling of High Temperature Superconductors (HTSs) is crucial for real-time quench monitoring; however, full-order electromagnetic simulations remain prohibitively costly due to the strong nonlinearities. Conventional projection-based reduced-order modeling pipelines for nonlinear problems, such as Proper Orthogonal Decomposition (POD)-Discrete Empirical Interpolation Method (DEIM), alleviate this cost but often require intrusive access to the Full Order Model (FOM) operators and a substantial number of interpolation points for hyperreduction. This work investigates reduced-order strategies for Integral Equation Method (IEM) of (HTS) systems. We present the first application of POD-DEIM to IEM-based HTS models, and introduce a Structured Neural Ordinary Differential Equation (Neural ODE) approach that learns nonlinear dynamics directly in the reduced space. The benchmark results show that Neural ODE outperforms POD-DEIM both in efficiency and accuracy, highlighting its potential for real-time simulations of superconductors.

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Riccardo Basei, Francesco Pase, Francesco Lucchini, Francesco Toso, Riccardo Torchio. 2025-10-16. A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes. https://doi.org/10.1109/tasc.2026.3657176

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