3 citations · 9 across the 12 of their papers we have counts for
5 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…
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…
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…