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Coarsening dynamics of fingerprint labyrinthine patterns: Machine learning–assisted characterization

Supriyo Ghosh1,2, Vinicius Yu Okubo3, Kotaro Shimizu4, B. S. Shivaram1, Hae Yong Kim3, and Gia-Wei Chern1

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Phys. Rev. E 114, 024208 – Published 12 August, 2026

DOI: https://doi.org/10.1103/djsd-6fkg

Abstract

Fingerprint labyrinthine patterns exhibit a level of structural complexity beyond simple stripe phases, combining local stripe order with a dense network of pointlike defects. Unlike symmetry-breaking phases, where coarsening proceeds via diffusive defect annihilation, or conventional stripe phases, where curvature-driven motion of extended grain boundaries dominates, the coarsening of fingerprint labyrinths is governed primarily by localized junction and terminal defects. Using the Turing-Swift-Hohenberg equation, we study the nonequilibrium relaxation of fingerprint labyrinthine patterns following a quench. To go beyond conventional Fourier-based diagnostics, we employ a template-matching convolutional neural network to identify and track junctions and terminals directly in real space, enabling a quantitative characterization of defect statistics and spatial correlations. We show that, although these pointlike defects drive coarsening, their motion is strongly constrained by the surrounding stripe geometry, leading to slow, nondiffusive dynamics that are qualitatively distinct from both conventional phase ordering and stripe coarsening. Together, these results establish defect-mediated dynamics as the central organizing principle of fingerprint labyrinthine coarsening and demonstrate the effectiveness of machine learning–assisted approaches for complex pattern-forming systems.

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Information

Phys. Rev. E 114, 024208– Published 12 August, 2026
  • Received 31 January 2026
  • Accepted 16 June 2026
Article part of CHORUS
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