Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography

TL;DR

This paper introduces a physically-informed probabilistic framework for Bayesian inverse problems in solid mechanics, explicitly modeling model discrepancies to improve uncertainty quantification and validation in elastography.

Contribution

It proposes an undirected probabilistic model that incorporates governing equations directly, moving beyond black-box approaches, and develops a scalable inference method for complex high-dimensional problems.

Findings

Effective in synthetic elastography problems with and without model error

Demonstrates improved model discrepancy quantification

Shows scalability of the inference approach

Abstract

The present paper is motivated by one of the most fundamental challenges in inverse problems, that of quantifying model discrepancies and errors. While significant strides have been made in calibrating model parameters, the overwhelming majority of pertinent methods is based on the assumption of a perfect model. Motivated by problems in solid mechanics which, as all problems in continuum thermodynamics, are described by conservation laws and phenomenological constitutive closures, we argue that in order to quantify model uncertainty in a physically meaningful manner, one should break open the black-box forward model. In particular we propose formulating an undirected probabilistic model that explicitly accounts for the governing equations and their validity. This recasts the solution of both forward and inverse problems as probabilistic inference tasks where the problem's state variables should not only be compatible with the data but also with the governing equations as well. Even though the probability densities involved do not contain any black-box terms, they live in much higher-dimensional spaces. In combination with the intractability of the normalization constant of the undirected model employed, this poses significant challenges which we propose to address with a linearly-scaling, double-layer of Stochastic Variational Inference. We demonstrate the capabilities and efficacy of the proposed model in synthetic forward and inverse problems (with and without model error) in elastography.