Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review-Reference-Cited by-同舟云学术

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

Published:2023 Issue:12 Volume:10 Page:5436-5456
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Lee Junhyeong¹,Park Donggeun¹,Lee Mingyu¹,Lee Hugon¹,Park Kundo¹,Lee Ikjin¹,Ryu Seunghwa¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea

Abstract

This review offers a guideline for selecting the ML-based inverse design method, considering data characteristics and design space size. It categorizes challenges and underscores the proper methods, with a focus on composites and its manufacturing.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/MH/D3MH00039G

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