English

An adaptive phase field framework for large-scale interface evolution problems using a strong-form gradient smoothing approach

Numerical Analysis 2026-07-27 v1 Mathematical Physics

Abstract

Multiscale problems with evolving interfaces are ubiquitous in science and engineering. Phase-field models are a powerful tool for simulating interface-dominated phenomena in computational mechanics and materials modeling, but their application to large-scale problems is often constrained by the high computational cost of resolving thin diffuse interfaces over the entire domain. This paper presents an efficient strong-form phase-field solver that couples the Gradient Smoothing Method (GSM) with a hierarchical adaptive and moving structured mesh, enabling automatic localization of resolution within a narrow interfacial region while retaining coarse discretization in bulk domains. A layered refinement design is introduced to preserve locally uniform resolution across the interface, allowing the GSM discretization to maintain overall second-order accuracy despite strong mesh non-uniformity away from the interface. Although GSM incurs a higher per-degree-of-freedom cost than standard finite-difference schemes, the adaptive framework substantially reduces the total number of degrees of freedom, resulting in near-linear computational scaling compared with the quadratic scaling of uniform-grid approaches. Numerical examples based on the Allen-Cahn and Cahn-Hilliard equations demonstrate that the proposed adaptive GSM solver delivers desired accuracy for interface evolution while attaining more favorable computational complexity, O(N), than existing weak-form and strong-form solvers, becoming significantly more efficient for large-scale problems with thin interfaces or a small interfacial area fraction relative to the whole domain.

Cite

@article{arxiv.2607.25142,
  title  = {An adaptive phase field framework for large-scale interface evolution problems using a strong-form gradient smoothing approach},
  author = {Zirui Mao and Alice Xu and Shenyang Hu and Panos Stinis and Yuyan Shao and Ang Li and Yulan Li},
  journal= {arXiv preprint arXiv:2607.25142},
  year   = {2026}
}