3 citations · 9 across the 12 of their papers we have counts for
4 papers · 1 filter
Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
Arnaud Vadeboncoeur, Gregory Duthé, Mark Girolami +1
Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of physical systems from a small number of n…
Retrieval-augmented reasoning with lean language models
Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas +6
This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG…
Efficient Deconvolution in Populational Inverse Problems
Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart
This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations. The potential to solve such distribu…
Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification
Pengyu Zhang, Connor Duffin, Alex Glyn-Davies +2
Super-resolution (SR) is a promising tool for generating high-fidelity simulations of physical systems from low-resolution data, enabling fast and accurate predictions in engineeri…