Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Denoising diffusion models have recently emerged as a powerful class of generative
models. They provide state-of-the-art results, not only for unconditional simulation, but also …

Triangular map is a recent construct in probability theory that allows one to transform any
source probability density function to any target density function. Based on triangular maps …

We introduce a new framework for efficient sampling from complex probability distributions,
using a combination of transport maps and the Metropolis--Hastings rule. The core idea is to …

We propose to compute a sparse approximate inverse Cholesky factor L of a dense
covariance matrix Θ by minimizing the Kullback--Leibler divergence between the Gaussian …