论文标题
在连续微力学的背景下，使用生成对抗神经网络生成三维微观结构
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
论文作者
论文摘要
多尺度模拟在计算资源方面要求。在连续微力学的背景下，多尺度问题源于从微观尺度上推断宏观材料参数。如果通过微观扫描明确给出了基础微观结构，则可以使用卷积神经网络来学习微观结构 - 绘图映射，这通常是从计算同质化获得的。 CNN方法提供了显着的加速，尤其是在异质或功能分级材料的背景下。另一个应用是不确定性量化，需要进行许多广泛的评估。但是，这种方法的一种瓶颈是所需的大量训练微观结构。这项工作通过提出针对三维微观结构生成的生成对抗网络来缩小这一差距。轻量级算法能够从单个Microct扫描中学习材料的基本属性，而无需明确的描述符。在预测时间内，网络可以在一秒钟内且始终高质量地生成具有原始数据具有相同特性的独特的三维微观结构。
Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macroscopic material parameters from the microscale. If the underlying microstructure is explicitly given by means of microCT-scans, convolutional neural networks can be used to learn the microstructure-property mapping, which is usually obtained from computational homogenization. The CNN approach provides a significant speedup, especially in the context of heterogeneous or functionally graded materials. Another application is uncertainty quantification, where many expansive evaluations are required. However, one bottleneck of this approach is the large number of training microstructures needed. This work closes this gap by proposing a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors. During prediction time, the network can produce unique three-dimensional microstructures with the same properties of the original data in a fraction of seconds and at consistently high quality.
