# Tutorial for conditional generative adversarial network

https://mdr.nims.go.jp/datasets/14268605-d006-4212-b2d4-c2274ca555de

## File

- [readme.pdf](https://mdr.nims.go.jp/filesets/ded8f5ce-2204-458c-acda-24180058c672/download) ([Detail](https://mdr.nims.go.jp/filesets/ded8f5ce-2204-458c-acda-24180058c672.md))
- [solver.py](https://mdr.nims.go.jp/filesets/1c67a371-32f7-44a1-8201-a6eb814f83f4/download) ([Detail](https://mdr.nims.go.jp/filesets/1c67a371-32f7-44a1-8201-a6eb814f83f4.md))
- [CGAN_main.py](https://mdr.nims.go.jp/filesets/5487c715-6546-408e-a2a6-b8ebcf248fb0/download) ([Detail](https://mdr.nims.go.jp/filesets/5487c715-6546-408e-a2a6-b8ebcf248fb0.md))
- [generate_geometies_using_trained_cgan.py](https://mdr.nims.go.jp/filesets/e608fde8-8c44-4c83-88d1-cfa2ab793f75/download) ([Detail](https://mdr.nims.go.jp/filesets/e608fde8-8c44-4c83-88d1-cfa2ab793f75.md))

## Id

14268605-d006-4212-b2d4-c2274ca555de

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-11T03:47:13.814838Z

## Updated at

2024-01-05T13:11:25.376576Z

## Published at

2023-03-20T07:27:14.117389Z

## Doi

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

## First published url

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

## Date published

2021-10-18

## Recorded date published

2021-12

## Resource type

software

## Manuscript type

na

## Collection



## Title

- title: Tutorial for conditional generative adversarial network
  title_type: original
  lang: en

## Description

- description: '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. '
  description_type: abstract
  lang: en

## Creator

- name: Xiaoyang Zheng
  role: author
  orcid: https://orcid.org/0000-0002-9004-9216
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Ta-Te Chen
  role: author
- name: Xiaofeng Guo
  role: author
- name: Sadaki Samitsu
  role: author
- name: Ikumu Watanabe
  role: author

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Deep learning; Generative adversarial network; Inverse design; Microstructure;
    Mechanical metamaterial
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2021-10-15

## Other identifier(s)



## Data origin

- data_origin_type: informatics_and_data_science

## Embargo



## Journal

- title: Materials & Design
  issn: '02641275'
  volume: '211'
  article_number: '110178'

## Conference



## Related item



## Funding

- identifier: 22J11202
  funder_name: Japan Society for the Promotion of Science
  description: Grant-in-Aid for JSPS Fellows DC2

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



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## Fileset

- id: ded8f5ce-2204-458c-acda-24180058c672
  filename: readme.pdf
  content_type: application/pdf
  size: 497218
  md5: '086c0a5859ae5fce79e5bdef5b41e5cd'
- id: 1c67a371-32f7-44a1-8201-a6eb814f83f4
  filename: solver.py
  content_type: text/x-python
  size: 8916
  md5: ee755e3eac736fc7a5e062a1e83bbb9d
- id: 5487c715-6546-408e-a2a6-b8ebcf248fb0
  filename: CGAN_main.py
  content_type: text/x-python
  size: 14399
  md5: 6021b33b875d1577b585827668c8bc2c
- id: e608fde8-8c44-4c83-88d1-cfa2ab793f75
  filename: generate_geometies_using_trained_cgan.py
  content_type: text/x-python
  size: 6087
  md5: 982f5f54ac8f4ec7bd4b7062f13e49f2

## Thumbnail

fileset_id: ded8f5ce-2204-458c-acda-24180058c672
filename: readme.pdf