Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity

This paper addresses trajectory generation for physical systems whose continuous-time dynamics are observed only at discrete times. The proposed PhiBE-Flow framework directly estimates the probability velocity field induced by stochastic dynamics rather than learning only a transition mapping between consecutive observations. Its focus is generating trajectories consistent with the probability densities of physical systems under stochastic evolution.