Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.

翻译：针对海上失事物体等拉格朗日漂移的重建问题，由于数据中未解析的物理现象往往存在不确定性。通常通过引入随机性来克服这种漂移不确定性，但该方法需要为不确定性建模设定特定假设。我们提出一种纯数据驱动的框架，消除了这一约束条件，可在灵活环境中对概率性漂移进行建模。利用海洋环流模型模拟，通过模拟初始物体位置的不确定性生成物体位置的概率轨迹。在完全已知速度场的条件下，我们训练了为期一天的概率漂移仿真器，观测到与数值模拟结果高度吻合。测试了多种损失函数后，进一步通过训练信息不完整的模型来检验框架的鲁棒性。在这些更具挑战性的场景中，虽然评估模型对未见流场时的数据漂移效应显现，但仍观察到合理的预测结果。