本构方程
人工神经网络
边值问题
可塑性
功能(生物学)
计算机科学
材料科学
有限元法
人工智能
结构工程
数学
工程类
数学分析
复合材料
进化生物学
生物
作者
Hang Yang,Hai Qiu,Xiang Qian,Shan Tang,Xu Guo
出处
期刊:Journal of Applied Mechanics
[ASME International]
日期:2020-05-14
卷期号:87 (9)
被引量:39
摘要
Abstract In this paper, a data-driven approach for constructing elastoplastic constitutive law of microstructured materials is proposed by combining the insights from plasticity theory and the tools of artificial intelligence (i.e., constructing yielding function through ANN) to reduce the required amount of data for machine learning. Illustrative examples show that the constitutive laws constructed by the present approach can be used to solve the boundary value problems (BVPs) involving elastoplastic materials with microstructures under complex loading paths (e.g., cyclic/reverse loading) effectively. The limitation of the proposed approach is also discussed.
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