7 citations · 20 across the 10 of their papers we have counts for
12 papers
Measure Neural Mappings
Ricardo Baptista, Edoardo Calvello, Bohan Chen +2
We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operators on probability measures. E…
Analysis of a zeroth-order multiscale sampler: strong convergence, convergence of ergodic averages and weak convergence
Antonin Della Noce, Urbain Vaes
In many practical applications of Bayesian inference, it is difficult or undesirable to compute the gradient of the negative log-posterior, motivating the study of derivative-free…
A spectral approach to the narrow escape problem in two-dimensional domains
Louis Carillo, Tony Lelièvre, Thomas Normand +1
We study the law of the exit time and exit point of a Brownian motion in a two-dimensional domain with reflecting boundary conditions, except on small disjoint exit windows through…
The narrow escape problem in arbitrary dimension
Louis Carillo, Tony Lelièvre, Thomas Normand +2
The narrow escape problem is a prototypical example for studying entropic metastability, motivated by the analysis of biological and chemical systems. The problem concerns the dete…
Uniform-in-time propagation of chaos for Consensus-Based Optimization
Nicolai Gerber, Franca Hoffmann, Dohyeon Kim +1
We study the derivative-free global optimization algorithm Consensus-Based Optimization (CBO), establishing uniform-in-time propagation of chaos as well as an almost uniform-in-tim…
Dynamical reweighting for estimation of fluctuation formulas
Raphaël Gastaldello, Gabriel Stoltz, Urbain Vaes
We propose a variance reduction method for calculating transport coefficients in molecular dynamics using an importance sampling method via Girsanov's theorem applied to Green--Kub…
Uniform-in-time propagation of chaos for the Cucker--Smale model
Nicolai Jurek Gerber, Urbain Vaes
This paper presents an elementary proof of quantitative uniform-in-time propagation of chaos for the Cucker--Smale model under sufficiently strong interaction. The idea is to combi…
CBX: Python and Julia packages for consensus-based interacting particle methods
Rafael Bailo, Alethea Barbaro, Susana N. Gomes +4
We introduce CBXPy and ConsensusBasedX.jl, Python and Julia implementations of consensus-based interacting particle systems (CBX), which generalise consensus-based optimization met…
Mean-field limits for Consensus-Based Optimization and Sampling
Nicolai Jurek Gerber, Franca Hoffmann, Urbain Vaes
For algorithms based on interacting particle systems that admit a mean-field description, convergence analysis is often more accessible at the mean-field level. In order to transfe…
Wasserstein stability estimates for covariance-preconditioned Fokker-Planck equations
J. A. Carrillo, U. Vaes
We study the convergence to equilibrium of the mean field PDE associated with the derivative-free methodologies for solving inverse problems. We show stability estimates in the euc…
Mean-field limits for interacting diffusions with colored noise: phase transitions and spectral numerical methods
S. N. Gomes, G. A. Pavliotis, U. Vaes
In this paper we consider systems of weakly interacting particles driven by colored noise in a bistable potential, and we study the effect of the correlation time of the noise on t…
A linear, second-order, energy stable, fully adaptive finite-element method for phase-field modeling of wetting phenomena
B. Aymard, U. Vaes, M. Pradas +1
We propose a new numerical method to solve the Cahn-Hilliard equation coupled with non-linear wetting boundary conditions. We show that the method is mass-conservative and that the…