Microstructure reconstruction using entropic descriptors

TL;DR

This paper introduces a multi-scale microstructure reconstruction method using entropic descriptors that capture spatial inhomogeneity and complexity, providing a new approach different from traditional correlation functions.

Contribution

It proposes the use of entropic descriptors for stochastic optimization in microstructure reconstruction, offering an alternative to correlation-based methods.

Findings

Successful reconstruction of binary and greyscale microstructures

Entropic descriptors reveal structural information beyond correlation functions

Method effective across multiple length scales

Abstract

A multi-scale approach to the inverse reconstruction of a pattern's microstructure is reported. Instead of a correlation function, a pair of entropic descriptors (EDs) is proposed for stochastic optimization method. The first of them measures a spatial inhomogeneity, for a binary pattern, or compositional one, for a greyscale image. The second one quantifies a spatial or compositional statistical complexity. The EDs reveal structural information that is dissimilar, at least in part, to that given by correlation functions at almost all of discrete length scales. The method is tested on a few digitized binary and greyscale images. In each of the cases, the persuasive reconstruction of the microstructure is found.