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Arnaud Vadeboncoeur

2 accepted papers

2026

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later

ICLR 2026poster

Uncertainty Quantification (UQ) is paramount for inference in engineering applications. A common inference task is to recover full-field information of physical systems from a small number of noisy observations, a usually highly ill-posed problem. Critically, engineering systems often have complicat…

Cited by 1SourcecodeScholar
2023

Random Grid Neural Processes for Parametric Partial Differential Equations

ICML 2023poster

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 variational neural processes. We achieve this by assigning probability measures to the spatial domain, which allows us to…

Cited by 12SourcePDFScholar