Inverse problems deal with the quest for unknown causes of observed consequences,
based on predictive models, known as the forward models, that associate the former …

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent
family of approaches for solving these problems uses stochastic algorithms that sample from …

In the framework of solid mechanics, the task of deriving material parameters from
experimental data has recently re-emerged with the progress in full-field measurement …

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 …

Modern imaging methods rely strongly on Bayesian inference techniques to solve
challenging imaging problems. Currently, the predominant Bayesian computation approach …

Diffusion models have achieved tremendous success in generating high-dimensional data
like images, videos and audio. These models provide powerful data priors that can solve …

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 investigate the low-dimensional structure of deterministic transformations between
random variables, ie, transport maps between probability measures. In the context of …

Dynamical systems modeling, particularly via systems of ordinary differential equations, has
been used to effectively capture the temporal behavior of different biochemical components …

Bibliography Page 1 Bibliography [1] S. AGAPIOU, Aspects of Bayesian inverse problems, PhD
thesis, University of Warwick, UK, 2013. (Cited on p. 88) [2] S. AGAPIOU, JM BARDSLEY, O …