A deep learning–based method for the design of microstructural materials

• Published in: Structural and multidisciplinary optimization Vol. 61; no. 4; pp. 1417 - 1438
• Main Authors: Tan, Ren Kai, Zhang, Nevin L., Ye, Wenjing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Computational Mathematics and Numerical Analysis; Computer simulation; Deep learning; Engineering; Engineering Design; Inverse design; Machine learning; Microstructure; Optimization; Optimization techniques; Research Paper; Tensors; Theoretical and Applied Mechanics; Deep learning; Microstructural materials; Material design; Convolutional neural network; Generative adversarial network

Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufact...