A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908835072966656 |
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| author | Basei, Riccardo Pase, Francesco Lucchini, Francesco Toso, Francesco Torchio, Riccardo |
| author_facet | Basei, Riccardo Pase, Francesco Lucchini, Francesco Toso, Francesco Torchio, Riccardo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14487 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes Basei, Riccardo Pase, Francesco Lucchini, Francesco Toso, Francesco Torchio, Riccardo Computational Engineering, Finance, and Science 65F30, 68T07 (Primary), 65R20 (Secondary) 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. |
| title | A Structured Neural ODE Approach for Real Time Evaluation of AC Losses in 3D Superconducting Tapes |
| topic | Computational Engineering, Finance, and Science 65F30, 68T07 (Primary), 65R20 (Secondary) |
| url | https://arxiv.org/abs/2510.14487 |