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March 28, 2026Communications in Mathematical Sciences0 citations

Cahn-Hilliard equations on lattices: dynamic transitions and pattern formations

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JGJared GrossmanEHEvan HalloranSWShouhong Wang

Key Points

  • This article aims to explore how geometry and physical parameters influence dynamic phase transitions and pattern formations in binary systems modeled by the Cahn-Hilliard equation.
  • Examined dynamic phase transitions in a two-dimensional lattice structure using Cahn-Hilliard equation.
  • Analyzed the influence of spanning vectors on dynamical transitions and pattern formations.
  • Decomposed function space into stable and unstable eigenspaces and calculated the center manifold.
  • Investigated geometry-dependent properties of critical eigenvalues and their multiplicities.
  • Considered long-range interaction model and its effects compared to the original model.
  • Observed emergence of hexagonally-packed circles, rolls, and square structures in non-rectangular domains.
  • Dynamic transitions were shown to correlate with the geometry of the domain and parameter choices.
  • Identified geometry-dependent multiplicities of critical eigenvalues affecting stability.

Abstract

This article examines the dynamic phase transitions and pattern formations attributed to binary systems modeled by the Cahn-Hilliard equation. In particular, we consider a two-dimensional lattice structure and determine how different choices of the spanning vectors influence the resulting dynamical transitions and pattern formations. As the basic steady-state loses its linear stability, the binary system undergoes a dynamic transition which is shown to be characterized by both the geometry of the domain and the choice of physical parameters of the model. Unlike rectangular domains, we are able to observe the emergence of hexagonally-packed circles, as well as the familiar rolls and square structures. We begin with the decomposition of our function space into a stable and unstable eigenspace before calculating the center manifold that maps the former to the later. In analyzing the resulting reduced equations, we consider the different multiplicities that the critical eigenvalue can have, which is shown to be geometry-dependent. We briefly consider the long-range interaction model and determine that it produces similar results to the original model.

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Cite This Study

Grossman et al. (2026) studied this question.

synapsesocial.com/papers/69c771198bbfbc51511e0e9ahttps://doi.org/10.4310/cms.260326185101
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