{
  "abstract": "Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description. We introduce a graph-recurrent neural network (GRNN) framework that learns such emergent dynamics directly from trajectories without specifying microscopic equations of motion. The framework integrates gated recurrent memory with graph message passing: local hidden states encode information beyond the instantaneous observables, while spatial coupling allows this information to propagate between sites. The observable and hidden fields evolve jointly through a shared local rule, providing a scalable representation of dynamics with spatial interactions and temporal memory. We demonstrate this framework on the emergent dynamics of charge-density-wave order in the semiclassical Holstein model, successfully reproducing temporal and spatial correlations and dynamical scaling during domain coarsening. By learning finite-time evolution directly, the GRNN offers a route to efficient, data-driven descriptions of collective dynamics with unresolved microscopic degrees of freedom.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.31350",
  "author_total": 3,
  "authors": [
    {
      "name": "Ali Rayat"
    },
    {
      "name": "Acacia Lee"
    },
    {
      "name": "Gia-Wei Chern"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7zqfrnc9zgwxr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.31350",
  "fulltext_url": null,
  "id": "7zqfrnc9zgwxr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.31350",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "cond-mat.str-el"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "openalex",
      "3107|Quantum many-body systems"
    ]
  ],
  "openalex": "W7223285850",
  "pdf_url": "https://arxiv.org/pdf/2610.31350",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7zqfrnc9zgwxr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:34Z",
      "native_id": "2610.31350",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.31350"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.str-el",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Graph-Recurrent Learning of Emergent Collective Dynamics",
  "title_text": "Graph-Recurrent Learning of Emergent Collective Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:34Z",
  "url": "https://research.draconitic.com/paper/7zqfrnc9zgwxr",
  "venue": "arXiv",
  "versions": []
}