[
    {
        "id": "osp-22355",
        "type": "article-journal",
        "title": "FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution",
        "author": [
            {
                "family": "Li",
                "given": "Pengyu"
            },
            {
                "family": "Tong",
                "given": "Renjie"
            },
            {
                "family": "Jiang",
                "given": "Xuanlue"
            },
            {
                "family": "Li",
                "given": "Jianmin"
            },
            {
                "family": "Zhou",
                "given": "Yuanyuan"
            }
        ],
        "URL": "https://omanscience.com/en/articles/flare-flow-matching-with-local-axis-angle-representations-for-stochastic-micromagnetic-evolution",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]