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This tutorial aims to give an introduction of how to use a deep generative model, conditional generative adversarial network (CGAN). The CGAN can be used for the inverse design of 2D and 3D microstructures with target properties. The CGAN is trained with supervised learning using a labeled dataset. The dataset consists of a large number of geometries and their corresponding properties (e.g., elastic moduli). After training, the CGAN can generate a batch of geometries using target properties at inputs. In our previous two papers, we have demonstrated how to use the CGAN for the inverse design of 2D auxetic metamaterials and 3D architected materials. We hope this tutorial can be useful for those who are interested in the inverse design problems of microstructures.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Deep learning; Generative adversarial network; Inverse design; Microstructure; Mechanical metamaterial

刊行年月日: 2021-10-18

出版者: Elsevier BV

掲載誌:

• Materials & Design (ISSN: 02641275) vol. 211 110178

研究助成金:

• Japan Society for the Promotion of Science 22J11202 (Grant-in-Aid for JSPS Fellows DC2)

原稿種別: 論文以外のデータ

MDR DOI: https://doi.org/10.48505/nims.3869

公開URL: https://doi.org/10.1016/j.matdes.2021.110178

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更新時刻: 2024-01-05 22:11:25 +0900

MDRでの公開時刻: 2023-03-20 16:27:14 +0900