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Machine Learning of Dislocation-Induced Stress Fields and Interaction Forces
JOM ( IF 2.6 ) Pub Date : 2020-10-09 , DOI: 10.1007/s11837-020-04389-w
Mohammad H. Rafiei , Yejun Gu , Jaafar A. El-Awady

In discrete dislocation dynamics (DDD) simulations dislocation-induced stress fields and dislocation–dislocation interaction forces are typically evaluated using analytically described multiparameter continuous functions. The universal approximation theory guarantees the approximation of such functions by some machine learning (ML) techniques, which in turn can potentially help to accelerate DDD simulations. However, accurate machine approximation is as crucial as its acceleration. Here, we demonstrate the feasibility of utilizing deep neural networks to predict dislocation-induced stress fields and dislocation–dislocation interaction forces. We also show that the trained network produces estimates that are in very good agreement with analytical solutions. This was only plausible by generating an enriched data repository to avoid bias in the training data. This work opens the door to further development of more optimized ML architectures that could lead to a more computationally efficient, yet accurate, approach to replace the generally inefficient analytical calculations of dislocation–dislocation interaction forces in DDD simulations.

中文翻译:

位错诱导应力场和相互作用力的机器学习

在离散位错动力学 (DDD) 模拟中,通常使用分析描述的多参数连续函数来评估位错引起的应力场和位错-位错相互作用力。通用逼近理论通过一些机器学习 (ML) 技术保证了这些函数的逼近,这反过来又可能有助于加速 DDD 模拟。然而,精确的机器逼近与其加速度一样重要。在这里,我们证明了利用深度神经网络来预测位错引起的应力场和位错-位错相互作用力的可行性。我们还表明,经过训练的网络产生的估计值与解析解非常一致。这只有通过生成丰富的数据存储库以避免训练数据中的偏差才合理。这项工作为进一步开发更优化的 ML 架构打开了大门,这可能会导致计算效率更高但更准确的方法来取代 DDD 模拟中通常低效的位错 - 位错相互作用力分析计算。
更新日期:2020-10-09
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