Predicting material microstructure evolution via data-driven machine learning

Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by the practical needs to accelerate the materials design process and deal with incomplete information in the real world of microstructure simulation.

Materials Science Computer Science Engineering Medicine

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Predicting material microstructure evolution via data-driven machine learning

Semantic Scholar · Materials Science · 2021

Abstract

Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by the practical needs to accelerate the materials design process and deal with incomplete information in the real world of microstructure simulation.

Source of record · Semantic Scholar

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Semantic Scholar

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