Apr 5, 2022 · We introduce a deep learning accelerated methodology to solve PDE-based Bayesian inverse problems with guaranteed accuracy.

Abstract. We introduce a deep learning accelerated methodology to solve PDE-based Bayesian inverse problems with guaranteed accuracy.

Sep 1, 2023 · We introduce a deep learning accelerated methodology to solve PDE-based Bayesian inverse problems with guaranteed accuracy.

We study the Bayesian inverse problem of inferring the Biot number, a spatio-temporal heat-flux parameter in a PDE model. This is an ill-posed problem where ...

To achieve accelerated Bayesian inference we develop a novel training scheme that uses data to adaptively train a neural-network surrogate simulating the ...

Bayesian Inference with Markov Chain Monte Carlo requires efficient computation of the likelihood function. In some scientific applications, the likelihood must ...

Abstract:We study the Bayesian inverse problem of inferring the Biot number, a spatio-temporal heat-flux parameter in a PDE model.

Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems.

Dive into the research topics of 'Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in ...

Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems. Teo Deveney, Eike Mueller ...