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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Deep learning; Generative adversarial network; Inverse design; Microstructure; Mechanical metamaterial

Date published: 2021-10-19

Publisher: Elsevier BV

Journal:

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

Funding:

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

Manuscript type: Not a journal article

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

First published URL: https://doi.org/10.1016/j.matdes.2021.110178

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Updated at: 2024-01-05 22:11:25 +0900

Published on MDR: 2023-03-20 16:27:14 +0900