标题： COSMOS：一种面向化学羽流的数据驱动概率时间序列模拟器，适用于空间尺度 显示英文标题

标题： COSMOS: A Data-Driven Probabilistic Time Series simulator for Chemical Plumes across Spatial Scales

摘要： 针对自动化环境监测应用中鲁棒气味导航策略的发展需求，需要对移动于大空间尺度上的代理的气味时间序列进行逼真的模拟。传统依赖计算流体力学（CFD）方法的方法可以捕捉气味羽流的时空动态，但由于其计算成本过高，在大规模模拟中并不实用。另一方面，基于 puff 的模拟虽然在大规模下具有计算可行性并且能够捕捉羽流的随机性，但无法再现自然的气味统计数据。在这里，我们提出了 COSMOS（可配置气味模拟模型在可扩展空间上），这是一种以数据驱动的概率框架，从真实数据集的空间和时间特征合成逼真的气味时间序列。COSMOS 生成的关键统计特征（如气味脉冲频率、持续时间和浓度）的分布与真实数据中观察到的相似，同时大幅降低了计算开销。通过在各种流动条件和尺度上重现关键统计属性，COSMOS 能够开发和评估基于代理的导航策略，并提供自然的气味体验。为了展示其效用，我们将暴露于 CFD 生成的羽流中的气味追踪代理与 COSMOS 模拟进行比较，结果显示两者在气味体验和产生的行为方面都非常相似。 显示英文摘要

摘要： The development of robust odor navigation strategies for automated environmental monitoring applications requires realistic simulations of odor time series for agents moving across large spatial scales. Traditional approaches that rely on computational fluid dynamics (CFD) methods can capture the spatiotemporal dynamics of odor plumes, but are impractical for large-scale simulations due to their computational expense. On the other hand, puff-based simulations, although computationally tractable for large scales and capable of capturing the stochastic nature of plumes, fail to reproduce naturalistic odor statistics. Here, we present COSMOS (Configurable Odor Simulation Model over Scalable Spaces), a data-driven probabilistic framework that synthesizes realistic odor time series from spatial and temporal features of real datasets. COSMOS generates similar distributions of key statistical features such as whiff frequency, duration, and concentration as observed in real data, while dramatically reducing computational overhead. By reproducing critical statistical properties across a variety of flow regimes and scales, COSMOS enables the development and evaluation of agent-based navigation strategies with naturalistic odor experiences. To demonstrate its utility, we compare odor-tracking agents exposed to CFD-generated plumes versus COSMOS simulations, showing that both their odor experiences and resulting behaviors are quite similar.