A multiscale approach to the stationary Ginzburg–Landau equations of superconductivity

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Authors: Christian Döding, Benjamin Dörich, Patrick Henning
Cited by: 0

Abstract

Abstract In this work, we study the numerical approximation of minimizers of the Ginzburg–Landau free energy, a common model to describe the behavior of superconductors under magnetic fields. The unknowns are the order parameter, which characterizes the density of superconducting charge carriers, and the magnetic vector potential, which allows to deduce the magnetic field that penetrates the superconductor. Physically important and numerically challenging are especially settings which involve lattices of quantized vortices which can be formed in materials with a large Ginzburg–Landau parameter $$\kappa $$ κ . In particular, $$\kappa $$ κ introduces a severe mesh resolution condition for numerical approximations. In order to reduce these computational restrictions, we investigate a particular discretization which is based on mixed meshes where we apply a Lagrange finite element approach for the vector potential and a localized orthogonal decomposition (LOD) approach for the order parameter. We justify the proposed method by a rigorous a-priori error analysis (in $$L^2$$ L 2 and $$H^1$$ H 1 ) in which we keep track of the influence of $$\kappa $$ κ in the main error contributions. This allows us to conclude $$\kappa $$ κ -dependent resolution conditions for the various meshes and which only impose moderate practical constraints compared to a conventional finite element discretization. While our results only provide information on the approximability of the minimizers, we conclude by further proposing a minimization procedure to illustrate our theoretical findings by numerical experiments.

Fields of Study & Subjects

Advanced Thermodynamics and Statistical Mechanics Advanced Mathematical Modeling in Engineering Theoretical and Computational Physics

Publication details

Journal / source
Numerische Mathematik
Volume
158
Issue
4
Pages
1291–1360
Publication date
16 May 2026
Language
EN