.png){kind=icon}
返回
Topology optimization using super-resolution image reconstruction methods
.png){kind=icon}
OA
AI
DOI：10.1016/j.advengsoft.2023.103413.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a new topology optimization method to obtain super-resolution images without increasing mesh refinement by using various methods. For traditional process, low-resolution (LR) images are fed into the Solid Isotropic Material with Penalization (SIMP) and Optimality Criteria (OC) methods. Here, the trained super-resolution images are added to the inner loops to reconstruct the topology and used to obtain high -resolution (HR) images from the LR images at the end of each iteration. After finishing the reconstruction process, the main topology optimization method recovers the original size images from the HR images for the next iteration. Several examples are presented to demonstrate the effectiveness of the proposed method. The final topologies provide noticeably improvement over those of typical SIMP method and create a much sharper and higher contrast images. Moreover, the proposed strategy using the super-resolution image reconstruction methods can give valuable innovation for conventional topology optimization process.
Keyword:
Super-resolution
Topology optimization
Single-material
Multi-material
SIMP
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
3.3K
被引数:
1.2W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Current understanding of the heavy fermion superconductivity in PrOs4Sb12当前对PrOs4Sb12中重费米子超导电性的理解
PREAI
分享收藏
查看更多内容