The authors introduce [SF]2M, a simulation-free objective for inferring stochastic dynamics efficiently. By leveraging entropic optimal transport and neural networks, [SF]2M outperforms existing methods in modeling Schrödinger bridges accurately.
The author proposes a Weak Collocation Regression method to reveal unknown stochastic dynamical systems with both α-stable Lévy noise and Gaussian noise, demonstrating accuracy and computational efficiency.
Accurately inferring the dynamics of biological processes from cross-sectional data requires accounting for intrinsic noise, especially molecular noise, and the Probability Flow Inference (PFI) method offers a computationally efficient way to achieve this by leveraging score-based generative models and incorporating realistic noise priors.