Simulation-Based Inference of Ginzburg--Landau Parameters in Type--1.5 Superconductors
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917118395547648 |
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| author | Kao, Jung-Shen |
| author_facet | Kao, Jung-Shen |
| contents | Inferring microscopic couplings in multi-component superconductors directly from vortex configurations is a challenging inverse problem. In Type-1.5 systems, Time-Dependent Ginzburg-Landau (TDGL) dynamics generate complex, glassy vortex patterns with high metastability. We explicitly quantify this intractability by analyzing the Hessian spectrum of the energy landscape, revealing a proliferation of soft modes that hinders traditional sampling. We address this challenge by combining a differentiable TDGL solver with Simulation-Based Inference (SBI). Our approach treats the solver as a stochastic forward model mapping physical parameters (θ = (η, B, ν)) to vortex density fields. Using Neural Ratio Estimation (NRE), we train a classifier to approximate the likelihood-to-evidence ratio and perform Bayesian inference for the interband Josephson coupling from vortex density fields. On synthetic data, the proposed method reliably recovers the coupling with calibrated uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation-Based Inference of Ginzburg--Landau Parameters in Type--1.5 Superconductors Kao, Jung-Shen Superconductivity Strongly Correlated Electrons Inferring microscopic couplings in multi-component superconductors directly from vortex configurations is a challenging inverse problem. In Type-1.5 systems, Time-Dependent Ginzburg-Landau (TDGL) dynamics generate complex, glassy vortex patterns with high metastability. We explicitly quantify this intractability by analyzing the Hessian spectrum of the energy landscape, revealing a proliferation of soft modes that hinders traditional sampling. We address this challenge by combining a differentiable TDGL solver with Simulation-Based Inference (SBI). Our approach treats the solver as a stochastic forward model mapping physical parameters (θ = (η, B, ν)) to vortex density fields. Using Neural Ratio Estimation (NRE), we train a classifier to approximate the likelihood-to-evidence ratio and perform Bayesian inference for the interband Josephson coupling from vortex density fields. On synthetic data, the proposed method reliably recovers the coupling with calibrated uncertainty. |
| title | Simulation-Based Inference of Ginzburg--Landau Parameters in Type--1.5 Superconductors |
| topic | Superconductivity Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2512.02411 |