发表机构
Southern University of Science and Technology（南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FLARE提出一种基于局部轴角表示的流匹配框架，直接生成全场随机磁化端点，无需逐步积分，在精度和速度上显著优于现有基线。
AI 中文摘要
长时程微磁模拟仍然昂贵，因为传统和学习的求解器通常逐步传播Landau-Lifshitz-Gilbert（LLG）动力学。现有的学习方法通常保留逐步积分或建模确定性演化，使得全场、直接时域随机预测在很大程度上未被探索。我们提出FLARE，一种流匹配框架，将随机有限时间磁化预测重新构建为基于锚点相对局部轴角旋转的条件传输。这种旋转空间公式尊重磁化动力学的内在几何结构，并通过构造保持逐点单位范数。通过显式条件化物理预测时域，FLARE直接生成多个目标时间点的全场随机端点，无需逐步积分。与每个指标上最强单检查点外部基线相比，FLARE实现了29.9%更低的角能量距离（15.30°），以及37.3%更低的公平能量分数（0.393）。在代表性的组合5纳秒两段协议上，FLARE在单个GPU上实现了比广泛使用的GPU微磁求解器MuMax$^3$高达3062倍的最佳批次加速。
英文摘要
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.
