probability-paths

This paper introduces a framework for active timepoint selection to infer probability paths from sparse snapshots, using linearized optimal transport to map distributions into a tangent space for Gaussian Process modeling, thereby enabling uncertainty-aware acquisition policies.

Introduces Lagrangian flow matching, a physics-based framework that uses the least-action principle to design probability paths and velocity fields for generative modeling, generalizing existing optimal-transport and diffusion paths.