3 citations · 9 across the 12 of their papers we have counts for
13 papers
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…
Statistical Finite Elements via Interacting Particle Langevin Dynamics
Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite +2
In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the st…
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz +2
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantifi…
Autoencoders in Function Space
Justin Bunker, Mark Girolami, Hefin Lambley +2
Autoencoders have found widespread application in both their original deterministic form and in their variational formulation (VAEs). In scientific applications and in image proces…
Efficient Prior Calibration From Indirect Data
O. Deniz Akyildiz, Mark Girolami, Andrew M. Stuart +1
Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four in…
Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
Lawrence A. Bull, Chiho Jeon, Mark Girolami +3
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, represe…
Improving embedding of graphs with missing data by soft manifolds
Andrea Marinoni, Pietro Lio', Alessandro Barp +2
Embedding graphs in continous spaces is a key factor in designing and developing algorithms for automatic information extraction to be applied in diverse tasks (e.g., learning, inf…
The Bayesian approach to inverse Robin problems
Aksel Kaastrup Rasmussen, Fanny Seizilles, Mark Girolami +1
In this paper we investigate the Bayesian approach to inverse Robin problems. These are problems for certain elliptic boundary value problems of determining a Robin coefficient on…
Riemannian Laplace Approximation with the Fisher Metric
Hanlin Yu, Marcelo Hartmann, Bernardo Williams +2
Laplace's method approximates a target density with a Gaussian distribution at its mode. It is computationally efficient and asymptotically exact for Bayesian inference due to the…
Tweedie Moment Projected Diffusions For Inverse Problems
Benjamin Boys, Mark Girolami, Jakiw Pidstrigach +3
Diffusion generative models unlock new possibilities for inverse problems as they allow for the incorporation of strong empirical priors in scientific inference. Recently, diffusio…