AI 中文总结
该研究针对流场化学源定位问题，基于浓度场与拉格朗日示踪剂轨迹的对偶性，提出结合薛定谔桥与朗之万动力学的反向输运框架，在二维湍流嗅觉搜索中性能优于经典策略。
AI 中文摘要
我们解决了流场中化学源定位的问题。基于浓度场与拉格朗日示踪剂轨迹之间的对偶性，我们将浓度检测解释为连接源与检测点的路径证据。这种在合理发射位置与检测点之间的薛定谔桥公式，利用被动示踪剂的反向传播子，将源定位构建为通过朗之万动力学对候选发射位置进行采样的过程。相关漂移揭示了经典趋化性与“抛投- surge”（抛投与突进）是从单一输运原理中涌现出的互补行为。将该方法应用于二维湍流中的嗅觉搜索，所提出的反向追踪框架在不同风况下，使用单一伽利略不变性的学习传播子，性能优于经典策略。
英文摘要
We address the problem of locating a chemical source in a flow. Based on the duality between the concentration field and Lagrangian tracer trajectories, we interpret concentration detections as evidence of paths connecting the source to the detection points. This Schrödinger bridge formulation between plausible emission positions and detection points leverages the backward propagator of passive tracers to frame source localization as the sampling of candidate emission locations via Langevin dynamics. The associated drift reveals classical chemotaxis and cast-and-surge as complementary behaviors emerging from a single transport-based principle. Applied to olfactory search in two-dimensional turbulence, the proposed backtracking framework outperforms classical strategies across varying wind regimes using a single, Galilean-invariant learned propagator.
