Introduces BALLAST, a Bayesian active learning framework with look-ahead amendment for optimizing Lagrangian observer placement to infer spatio-temporal vector fields via physics-informed GPs, with benefits shown on synthetic and ocean models plus a new VaSE GP inference method.
A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation
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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Introduces BALLAST, a Bayesian active learning framework with look-ahead amendment for optimizing Lagrangian observer placement to infer spatio-temporal vector fields via physics-informed GPs, with benefits shown on synthetic and ocean models plus a new VaSE GP inference method.