31 citations · 37 across the 8 of their papers we have counts for
10 papers
Generative Priors Conditioned on Natural Language for Bayesian Inversion in PDEs
Pengyu Zhang, Mark Girolami, Arnaud Vadeboncoeur
Inferring quantities of interest (QoI) from data is a central task in Science and Engineering. In such contexts, we often have access to both quantitative data and qualitative data…
Distributional Inverse Homogenization
Arnaud Vadeboncoeur, Mark Girolami, Kaushik Bhattacharya +1
For many materials, macroscopic mechanical behavior is determined by an intricate microstructure. Understanding the relation between these two scales helps scientists and engineers…
Hierarchical Inference and Closure Learning via Adaptive Surrogates for ODEs and PDEs
Pengyu Zhang, Arnaud Vadeboncoeur, Alex Glyn-Davies +1
Inverse problems are the task of calibrating models to match data. They play a pivotal role in diverse engineering applications by allowing practitioners to align models with reali…
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…
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…
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…
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…
Random Grid Neural Processes for Parametric Partial Differential Equations
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou +3
We introduce a new class of spatially stochastic physics and data informed deep latent models for parametric partial differential equations (PDEs) which operate through scalable va…
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite +2
We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric par…