BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
arXiv:2509.26005v3 Announce Type: replace-cross 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 t
The development of sophisticated AI techniques, particularly in spatio-temporal Gaussian process models, enables more precise and adaptive environmental sensing methods critical for climate monitoring.
Improving the accuracy and efficiency of marine data collection through AI-guided active learning is crucial for advancing oceanography and addressing climate-related challenges.
Traditional ad-hoc observer placement is replaced by a formal, AI-driven methodology, leading to more optimized and effective data acquisition for understanding complex marine systems.
- · Oceanographers
- · Marine science institutions
- · Ocean engineering firms
- · Environmental monitoring agencies
- · Traditionalists in environmental sensing methods
More accurate and efficient environmental data collection for oceanographic studies and climate models.
Improved predictive capabilities for ocean currents, pollution dispersal, and marine ecosystem health, enabling better resource management and disaster preparedness.
Potential for new industries focused on autonomous, AI-guided environmental surveying and real-time marine data services, impacting global trade routes and resource exploration.
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Read at arXiv cs.LG