Code: Learning non-equilibrium diffusions with Schrödinger bridges: from exactly solvable to simulation-free

This repository provides PyTorch code accompanying the paper "Learning non-equilibrium diffusions with Schrödinger bridges: from exactly solvable to simulation-free", presented at NeurIPS 2025.

• fm.py provides an implementation of the mvOU-OTFM algorithm (Alg. 1): LinearEntropicOTFM and LinearBridgeMatcher.
• EntropicOTFM and BridgeMatcher are corresponding implementations in the case of a Brownian reference.
• GaussianOUSB implements computation of the Gaussian Schrödinger bridge using the formulas of Theorem 2.
• notebooks provide code for reproducing experiments from the paper.

If this paper and code are useful for your own research, please consider citing our work:

Learning non-equilibrium diffusions with Schr\"odinger bridges: from exactly solvable to simulation-free
Stephen Y. Zhang and Michael Stumpf
The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025