A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes
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.
Cite
@article{arxiv.2510.14487,
title = {A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes},
author = {Riccardo Basei and Francesco Pase and Francesco Lucchini and Francesco Toso and Riccardo Torchio},
journal= {arXiv preprint arXiv:2510.14487},
year = {2026}
}
Comments
in IEEE Transactions on Applied Superconductivity, 2026