Probabilistic modelling and reconstruction of strain

TL;DR

This paper introduces a probabilistic Gaussian process model for strain field reconstruction from neutron Bragg-edge data, incorporating equilibrium constraints to improve accuracy and computational efficiency, demonstrated through simulations and real data.

Contribution

It presents a novel probabilistic framework for strain field modeling that integrates physical constraints, enhancing reconstruction accuracy and computational efficiency.

Findings

High potential demonstrated on simulations and real data

Effective incorporation of equilibrium constraints

Significant reduction in computational complexity

Abstract

This paper deals with modelling and reconstruction of strain fields, relying upon data generated from neutron Bragg-edge measurements. We propose a probabilistic approach in which the strain field is modelled as a Gaussian process, assigned a covariance structure customised by incorporation of the so-called equilibrium constraints. The computational complexity is significantly reduced by utilising an approximation scheme well suited for the problem. We illustrate the method on simulations and real data. The results indicate a high potential and can hopefully inspire the concept of probabilistic modelling to be used within other tomographic applications as well.