用机器学习提升颗粒材料建模精度与效率
Machine Learning Aided Modeling of Granular Materials: A Review
- 结合神经网络模拟颗粒间微观相互作用
- 对比多种模型对颗粒本构关系的学习效果
- 适合关注智能材料建模的工程师与研究者
自2017年谷歌AlphaGo击败世界冠军以来，人工智能（AI）成为热点。过去五年，作为AI子领域的机器学习在颗粒材料研究领域受到广泛关注。本文系统回顾了机器学习辅助颗粒材料建模的最新进展，涵盖从颗粒尺度的粒子-粒子相互作用与接触模型，到宏观颗粒流数值模拟的全过程。首先介绍机器学习在微观颗粒相互作用建模中的应用；其次综述并比较了用于学习颗粒材料本构行为的不同神经网络；最后讨论了基于神经网络与数值方法融合的工程实际或边界值问题的宏观仿真。希望读者通过本文全面了解机器学习在颗粒材料建模中的发展脉络。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.
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