Predicting material microstructure evolution via data-driven machine learning

被引：8
|
作者：
Kautz, Elizabeth J. [1 ]
机构：
[1] Pacific Northwest Natl Lab, Energy & Environm Directorate, Richland, WA 99352 USA
来源：
PATTERNS | 2021年 / 2卷 / 07期
基金：
英国工程与自然科学研究理事会;
关键词：
D O I：
10.1016/j.patter.2021.100285
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页数：2
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