Adaptivity in bayesian inverse finite element problems: learning and simultaneous control of discretisation and sampling errors

Kerfriden, Pierre ORCID: https://orcid.org/0000-0002-7749-3996, Kundu, Abhishek ORCID: https://orcid.org/0000-0002-8714-4087 and Claus, Susanne ORCID: https://orcid.org/0000-0001-9014-5278 2019. Adaptivity in bayesian inverse finite element problems: learning and simultaneous control of discretisation and sampling errors. Materials 12 (4) , 642. 10.3390/ma12040642

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (6MB) | Preview

Abstract

The local size of computational grids used in partial differential equation (PDE)-based probabilistic inverse problems can have a tremendous impact on the numerical results. As a consequence, numerical model identification procedures used in structural or material engineering may yield erroneous, mesh-dependent result. In this work, we attempt to connect the field of adaptive methods for deterministic and forward probabilistic finite-element (FE) simulations and the field of FE-based Bayesian inference. In particular, our target setting is that of exact inference, whereby complex posterior distributions are to be sampled using advanced Markov Chain Monte Carlo (MCMC) algorithms. Our proposal is for the mesh refinement to be performed in a goal-oriented manner. We assume that we are interested in a finite subset of quantities of interest (QoI) such as a combination of latent uncertain parameters and/or quantities to be drawn from the posterior predictive distribution. Next, we evaluate the quality of an approximate inversion with respect to these quantities. This is done by running two chains in parallel: (i) the approximate chain and (ii) an enhanced chain whereby the approximate likelihood function is corrected using an efficient deterministic error estimate of the error introduced by the spatial discretisation of the PDE of interest. One particularly interesting feature of the proposed approach is that no user-defined tolerance is required for the quality of the QoIs, as opposed to the deterministic error estimation setting. This is because our trust in the model, and therefore a good measure for our requirement in terms of accuracy, is fully encoded in the prior. We merely need to ensure that the finite element approximation does not impact the posterior distributions of QoIs by a prohibitively large amount. We will also propose a technique to control the error introduced by the MCMC sampler, and demonstrate the validity of the combined mesh and algorithmic quality control strategy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	1996-1944
Date of First Compliant Deposit:	28 February 2019
Date of Acceptance:	5 February 2019
Last Modified:	06 May 2023 10:05
URI:	https://orca.cardiff.ac.uk/id/eprint/119861

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item