Cosmos: A data-driven probabilistic time series simulator for chemical plumes across spatial scales

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.