Data-Efficient Deep Generative Model with Discrete Latent Representation for High-Fidelity Digital Materials-Reference-Cited by-同舟云学术

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Data-Efficient Deep Generative Model with Discrete Latent Representation for High-Fidelity Digital Materials

Published:2023-02-02 Issue:3 Volume:5 Page:730-737
ISSN:2639-4979
Container-title:ACS Materials Letters
language:en
Short-container-title:ACS Materials Lett.

Author:

Kim Namjung¹^ORCID,Lee Dongseok²^ORCID,Hong Youngjoon²

Affiliation:

1. Department of Mechanical Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam, Gyeonggi-do 13120, Republic of Korea

2. Department of Mathematics, Sungkyunkwan University, 2066 Seburo, Suwon, Gyeonggi-do 16419, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Biomedical Engineering,General Chemical Engineering

Link

https://pubs.acs.org/doi/pdf/10.1021/acsmaterialslett.2c01096

Reference45 articles.

1. Novel 2D metamaterials with negative Poisson’s ratio and negative thermal expansion

2. Isotropic Negative Thermal Expansion Metamaterials

3. Flexible self-powered multifunctional sensor for stiffness-tunable soft robotic gripper by multimaterial 3D printing

4. Recent advancements and applications in 3D printing of functional optics

5. Mechanical cloak design by direct lattice transformation

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2. A critical review on the application of machine learning in supporting auxetic metamaterial design;Journal of Physics: Materials;2024-03-21

3. An On-Demand Inverse Design Method for Nanophotonic Devices Based on Generative Model and Hybrid Optimization Algorithm;Plasmonics;2023-10-04

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