Do Epistemic Uncertainties Allow for Replacing Microstructural Experiments with Reconstruction Algorithms?

A novel problem in computational materials modeling is addressed: "Are the computational microstructure reconstruction techniques reliable enough to replace experiments?" Here, "reliable" computations are associated with producing "expected" reconstructions that are adequately close to the experimental data. The output of computational techniques can deviate from the experimental measurements because of the epistemic uncertainties in the algorithms. In this work, an analytical formulation for quantification of epistemic uncertainties in a microstructure reconstruction algorithm based on Markov random field is presented. The method is used to predict the large-scale spatial distribution of a microstructure given an experimental input measured over a small spatial domain. However, small variations are observed on the microstructural features of the synthesized samples due to the Markov random field algorithm. The proposed analytical technique aims to quantify these uncertainties and estimate their propagation to the macroscale material properties to provide a significant understanding on how reliable it is to replace the experiments with the Markov random field model to predict microstructural maps over large spatial domains.