We propose a procedure for estimating the Schr\" odinger bridge between two probability
distributions. Unlike existing approaches, our method does not require iteratively simulating …

We propose a simulation-free algorithm for the solution of generic problems in stochastic
optimal control (SOC). Unlike existing methods, our approach does not require the solution …

Flow matching has recently emerged as a powerful paradigm for generative modeling and
has been extended to probabilistic time series forecasting in latent spaces. However, the …

Sequential models like recurrent neural networks and transformers have become standard
for probabilistic multivariate time series forecasting across various domains. Despite their …

Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far
stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped …

Generative models based on dynamical transport of measure, such as diffusion models, flow
matching models, and stochastic interpolants, learn an ordinary or stochastic differential …

Conditioning diffusion and flow models have proven effective for super-resolving small-scale
details in natural images. However, in physical sciences such as weather, super-resolving …

In this manuscript, we present a novel approach for sampling from a continuous multivariate
probability distribution, which may either be explicitly known (up to a normalization factor) or …

We present a novel Bayesian learning framework for interacting particle systems with
discrete latent states, addressing the challenge of inferring dynamics from partial, noisy …

In this work, we present a unified approach to cosmological parameter inference and initial
condition reconstruction using Stochastic Interpolants and normalizing flows. We apply this …