我们提出一阶轨迹匹配（First-Order Trajectory Matching, FTM），一种代理建模方法，用于从随机系统的轨迹中学习概率质量的一阶局域输运特性。通过匹配轨迹的对称一阶运动，FTM 学习概率流速度（probability current velocity），其流场可保持时间边缘分布，从而匹配集成平均值，同时亦能刻画类流性质的轨迹量，例如通量、环流及越障电流。FTM 直接从轨迹数据中学习流速度，无需估计漂移项、扩散项或得分函数（score）。我们的稳定性分析将离散化误差与采样方差分离，并表明：当时间分辨率与样本量适当平衡时，无需单步仿真的 FTM 损失函数具有数值稳定性。在若干随机动力系统与偏微分方程（PDE）示例中，我们实证表明 FTM 能以低计算开销、确定性前向推演（deterministic-rollout）成本，提供轨迹感知的集成预测。

We introduce First-Order Trajectory Matching (FTM), a surrogate-modeling method that learns the first-order local transport of probability mass from trajectories of stochastic systems. By matching the symmetric first-order motion of trajectories, FTM learns the probability current velocity, whose flow preserves time marginals to match ensemble averages, while also capturing current-like trajectory quantities such as fluxes, circulations, and barrier-crossing currents. FTM learns the current velocity directly from trajectories, avoiding drift, diffusion, and score estimation. Our stability analysis separates discretization error from sampling variance and shows that the one-step simulation-free FTM loss is stable when temporal resolution and sample size are properly balanced. Across stochastic dynamical systems and PDE examples, we empirically demonstrate that FTM provides trajectory-aware ensemble predictions at low, deterministic-rollout cost.