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Simulation-Based Inference of Ginzburg--Landau Parameters in Type--1.5 Superconductors

JS Kao - arXiv preprint arXiv:2512.02411, 2025 - arxiv.org
Physics paper cond-mat.supr-con Suggest

… 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 …

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BibTeX

@article{2512.02411v1,
Author        = {Jung-Shen Kao},
Title         = {Simulation-Based Inference of Ginzburg--Landau Parameters in Type--1.5 Superconductors},
Eprint        = {2512.02411v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cond-mat.supr-con},
Abstract      = {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.},
Year          = {2025},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2512.02411v1},
File          = {2512.02411v1.pdf}
}

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