On the relevance of descriptor fidelity in microstructure reconstruction

A common strategy for reducing the computational effort of descriptor-based microstructure reconstruction in the Yeong–Torquato algorithm lies in restricting the choice of descriptors to an efficiently computable subset. As an alternative, the number of iterations can be reduced by gradient-based optimization as in differentiable microstructure characterization and reconstruction (DMCR). This allows for, but does not require, the use of a set of informative, high-dimensional and computationally expensive descriptors that would be unfeasible for a high number of iterations. For this reason, the present work investigates the role of descriptor fidelity on microstructure reconstruction results. More precisely, spatial two- and three-point correlations as well as the lineal path function are computed on 2D planes as well as on 1D lines. These descriptors are used for reconstruction with the Yeong–Torquato and DMCR algorithm and the results are compared throughout various microstructures, respectively.