Information-driven optimal experimental design with deep neural network surrogate model for composite materials-Reference-Cited by-同舟云学术

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Information-driven optimal experimental design with deep neural network surrogate model for composite materials

Published:2023-08-22 Issue:1 Volume:31 Page:210-217
ISSN:1537-6494
Container-title:Mechanics of Advanced Materials and Structures
language:en
Short-container-title:Mechanics of Advanced Materials and Structures

Author:

Jang Kyung Suk¹,Yun Gun Jin¹^ORCID

Affiliation:

1. Department of Aerospace Engineering, Seoul National University, Seoul, South Korea

Funder

Korea Evaluation Institute of Industrial Technology

Korea government

Institute of Engineering Research at Seoul National University

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science,General Mathematics,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15376494.2023.2242341

Reference29 articles.

1. Stacking design of uniaxial/biaxial braided composite tube under low-velocity impact load

2. Progressive damage modeling and optimization of fibrous composites under ballistic impact loading

3. Optimization design for circular multi-cell thin-walled tubes with discrete and continuous design variables

4. Optimal Design of Composite Granular Protectors

5. Mechanical properties and macro–meso coupled damage behavior of three-dimensional four-directional braided composites under bending loading

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1. Recent advances on nonlinear structure mechanics and failure analysis;Mechanics of Advanced Materials and Structures;2024-01-02

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