activity
20022026
most citedCoupled paraxial wave equations in random media in the white-noise regime

56 citations · 176 across the 22 of their papers we have counts for

collaborators

30 papers

math-ph2026

Imaging through rough interfaces: The shower curtain effect

Cristophe Gomez, Knut Sølna

The quality of an image observed through a scattering layer, such as a shower curtain, depends strongly on the relative position of the scattering layer between the object and the…

stat.ML2026

Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures

Lorenzo Baldassari, Josselin Garnier, Knut Solna +1

Obtaining stable diffusion-based samplers in high- and infinite-dimensional settings is challenging because errors can accumulate across high-frequency coordinates and make the dyn…

math-ph2026

Generalized Snell's laws for rough interfaces

Christophe Gomez, Knut Sølna

In this paper, we consider the reflection and transmission problem of waves by a rapidly oscillating rough interface that exhibits general mixing properties. Using an asymptotic an…

math.NA2026

Dimension-Free Multimodal Sampling via Preconditioned Annealed Langevin Dynamics

Lorenzo Baldassari, Josselin Garnier, Knut Solna +1

Designing sampling algorithms for multimodal targets that remain stable under refinement of the finite-dimensional approximation of an underlying function-space problem is a centra…

stat.ML2025

Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems

Lorenzo Baldassari, Josselin Garnier, Knut Solna +1

Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces - where such problems are naturally formulated - is cru…

stat.ML2024★ 2 cited

Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems

Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2

This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is co…