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Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning.

Serveh Kamrava¹, Muhammad Sahimi¹, Pejman Tahmasebi²

¹Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211, USA.

Physical Review. E

|May 20, 2020

Summary

This study introduces a machine learning method to evaluate material reconstruction techniques, overcoming limitations of traditional methods. It uses deep learning to assess the accuracy of material microstructures, enabling better comparisons of reconstruction algorithms.

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Area of Science:

Materials Science
Computational Modeling
Machine Learning

Background:

Acquiring numerous digital images of material microstructures is time-consuming and costly.
Stochastic methods are increasingly used for material morphology modeling from limited images.
Traditional evaluation methods like two-point correlation functions are often insufficient for assessing reconstruction accuracy.

Purpose of the Study:

To propose a novel machine learning-based method for evaluating stochastic reconstruction algorithms of material microstructures.
To address the limitations of existing evaluation techniques in distinguishing high- and low-quality reconstructions.
To provide a quantitative and computationally efficient approach for comparing material reconstruction methods.

Main Methods:

Utilized an unsupervised deep-learning algorithm to reduce the dimensionality of material images and their reconstructions.
Introduced two criteria for accuracy evaluation: internal uncertainty space (variation among realizations) and external uncertainty space (similarity to original).
Defined a final accuracy score as the ratio of two uncertainty indices to quantitatively compare stochastic algorithms.

Main Results:

The proposed machine learning method effectively evaluates stochastic reconstruction algorithms.
The internal uncertainty space criterion assesses the diversity of generated microstructures.
The external uncertainty space criterion quantifies the fidelity of reconstructions to the original material.
The ratio of uncertainty indices provides a reliable score for comparing reconstruction approaches.

Conclusions:

The developed machine learning approach offers a more accurate and efficient way to evaluate material microstructure reconstruction.
This method provides a quantitative basis for selecting superior stochastic reconstruction algorithms.
The technique is applicable to various stochastic reconstruction methods and heterogeneous materials.