This project will develop a novel observing system that uses artificial intelligence (AI) to coordinate multiple Autonomous Underwater Vehicles (AUVs). The system will be used to characterize the three-dimensional structure of tidally-driven coastal headland eddies, investigate spatiotemporal variability of the small-scale flow convergence within the eddies, and understand the underlying mechanisms and their influence on the dispersal of marine materials. This approach is designed to overcome computational barriers of traditional models to adaptive sampling aboard AUVs. The learning-based digital twin will be trained with a hydrodynamic model to approximate full model data assimilation for rapid short-horizon forecasts. This work will establish a framework for combining traditional hydrodynamic modeling with novel AI and machine learning methods to achieve a deeper understanding of key dynamics of coastal ocean processes using autonomous adaptive sampling. The strategically obtained in-situ data will be used with the hydrodynamic model to identify smaller-scale convergence streaks in the eddy, characterize their three-dimensional structure, and diagnose the mechanisms that drive their evolution. The result will further our ability to understand the contribution of transient eddies in aggregation or dispersal of marine constituents, such as coral larvae and pollutants. The constraints imposed by real-time use onboard AUVs necessitate new learning-based models that approxima