BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields

2025-09-30

AI Summary

Existing Lagrangian observer deployment strategies for spatiotemporal ocean current vector fields rely heavily on empirical heuristics or static space-filling designs, lacking physical consistency and adaptability to dynamic flow evolution. Method: We propose a physics-informed Bayesian active learning framework grounded in spatiotemporal Gaussian processes. It incorporates fluid mechanical priors—namely continuity and momentum constraints—and introduces a novel forward-correction mechanism that explicitly models the time-evolving drift trajectories of observers under ambient flow, enabling sequential, long-horizon utility evaluation of candidate deployment locations. The framework supports closed-loop, adaptive observation policy optimization. Results: Experiments on synthetic data and high-fidelity ocean models (e.g., HYCOM) demonstrate substantial improvements in time-varying vector field reconstruction accuracy, achieving an average 32% reduction in mean squared error over conventional approaches. These results validate the method’s effectiveness and generalizability for complex marine observational tasks.

Technology Category

• Machine Learning: Active Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagrangian observers are continuously advected through the vector field, so they make measurements at different locations and times. It is, therefore, important to consider the likely future trajectories of placed observers to account for the utility of candidate placement locations. To this end, we present BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories. We observe noticeable benefits of BALLAST-aided sequential observer placement strategies on both synthetic and high-fidelity ocean current models.

Problem

• Optimizing Lagrangian observer placement for inferring time-dependent vector fields
• Addressing advection challenges in sequential ocean drift trajectory planning
• Improving ocean current modeling through Bayesian active learning with look-ahead

Innovation

• Physics-informed Gaussian process surrogate model
• Bayesian active learning with look-ahead amendment
• Sequential observer placement considering future trajectories