均质化(气候)
生成对抗网络
对抗制
生成语法
边界(拓扑)
计算机科学
微观结构
边值问题
数学优化
人工智能
材料科学
数学
复合材料
数学分析
深度学习
生物多样性
生态学
生物
作者
Jicheng Li,Hongling Ye,Xing Zhang,Nan Wei
标识
DOI:10.1080/15376494.2022.2129888
摘要
An intelligent microstructural design method based on deep learning is proposed considering performance indicators that contains boundary information and homogenized elastic modules. Microstructure dataset is established by random boundary method and homogenization method. Random boundary method is proposed to design microstructures under given boundary information, and homogenization method is utilized to acquire homogenized elastic modules. A generative and adversarial network with gradient penalty is developed to establish the high-dimensional mapping between performance indicators and microstructure. The Wasserstein distance is imported to overcome mode collapse. Numerical simulation shows that the pre-trained network successfully achieved corresponding microstructure design by given performance indicators.
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