Fractal and Fractional, Volume 9, Issue 12
2025 December - 76 articles
Cover Story: Fractal extensions of the time-dependent Ginzburg–Landau equation offer a powerful framework for describing superconducting dynamics in non-differentiable space–time, but their numerical solution remains challenging. In this work, we develop two deep learning solvers for the fractal TDGL system: a physics-informed neural network embedding the Scale-Relativity covariant derivative, and a graph neural network learning gauge-covariant interactions on discrete spatial graphs. We show that the graph-based model improves vortex-core localization, magnetic-field reconstruction, and robustness to noise, enabling data-efficient modeling of fractal superconductivity. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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