Learning hidden-state dynamics from unpaired snapshots with lift-project SDEs
Qi Wang, Uduak George, Gustaaf Jacobs
Abstract
Many systems are observed only through unpaired snapshots, with population samples available at each time point but no correspondence between samples at different times. These snapshots determine the one-time distributions but leave the temporal coupling unspecified, requiring a modeling prior to connect successive times. We propose the lift-project stochastic differential equation (SDE), an autonomous SDE on the observed coordinate and a few hidden coordinates, trained only to match the snapshots. Most existing methods, including time-dependent SDEs, Schrödinger bridges and optimal transport, evolve the observed coordinate alone. For a scalar observation, the classical comparison theorem implies that all such models have non-negative lagged covariance, so on data whose lagged covariance turns negative, their error has a computable floor. The lift-project SDE is not bound by this floor. On two representative partially observed systems, the Duffing and pendulum oscillators, at comparable fit to the snapshots, the proposed method reduces the lagged-covariance error by factors of 2.4 and 6.6 relative to the strongest baseline and is the only model below the error floor; one hidden coordinate is already enough for such improvement. Furthermore, if a second experiment changes only the initial state of the hidden coordinate, no single model that evolves the observed coordinate alone can fit both experiments, whereas a single lift-project SDE can.