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Data-driven learning of 3-point correlation functions as microstructure representations
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DOI：10.1016/j.actamat.2022.117800.png)
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Abstract
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This paper considers the open challenge of identifying complete, concise, and explainable quantitative microstructure representations for disordered heterogeneous material systems. Completeness and conciseness have been achieved through existing data-driven methods, e.g., deep generative models, which, however, do not provide mathematically explainable latent representations. This study investigates representations composed of three-point correlation functions, which are a special type of spatial convolutions. We show that a variety of microstructures can be characterized by a concise subset of three-point correlations (100-fold smaller than the full set), and the identification of such subsets can be achieved by Bayesian optimization on a small microstructure dataset. The proposed representation can directly be used to compute material properties by leveraging the effective medium theory, allowing the construction of predictive structure-property models with significantly less data than needed by purely data-driven methods and with a computational cost 100-fold lower than the physics-based model.(c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Keywords:
Quantitative microstructure representation
Higher-order spatial correlations
Heterogeneous material reconstruction
Bayesian optimization
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