{
  "abstract": "Long-horizon micromagnetic simulation remains expensive because conventional and learned solvers typically propagate Landau--Lifshitz--Gilbert (LLG) dynamics step by step. Existing learned approaches generally retain stepwise integration or model deterministic evolution, leaving full-field, direct-horizon stochastic prediction largely unexplored. We propose FLARE, a flow-matching framework that recasts stochastic finite-time magnetization prediction as conditional transport over anchor-relative local axis-angle rotations. This rotation-space formulation respects the intrinsic geometry of magnetization dynamics and preserves pointwise unit norm by construction. By explicitly conditioning on the physical prediction horizon, FLARE directly generates full-field stochastic endpoints across multiple target times without stepwise integration. Against the strongest single-checkpoint external baseline on each metric, FLARE achieves 29.9% lower angular energy distance ($15.30^\\circ$), and a 37.3% lower fair energy score (0.393). On a representative composed 5-ns two-segment protocol, FLARE achieves a $3{,}062\\times$ best-batch speedup over the widely used GPU micromagnetic solver MuMax$^3$ on a single GPU.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34070",
  "author_total": 5,
  "authors": [
    {
      "name": "Pengyu Li"
    },
    {
      "name": "Renjie Tong"
    },
    {
      "name": "Xuanlue Jiang"
    },
    {
      "name": "Jianmin Li"
    },
    {
      "name": "Yuanyuan Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0z1jkbz4n0ks7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34070",
  "fulltext_url": null,
  "id": "0z1jkbz4n0ks7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34070",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mes-hall"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7214774298",
  "pdf_url": "https://arxiv.org/pdf/2609.34070",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0z1jkbz4n0ks7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.34070",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34070"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mes-hall",
      "subfield": "condensed-matter"
    }
  ],
  "title": "FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution",
  "title_text": "FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/0z1jkbz4n0ks7",
  "venue": "arXiv",
  "versions": []
}