Universal machine learning for topology optimization

2021 ◽  
Vol 375 ◽  
pp. 112739
Author(s):  
Heng Chi ◽  
Yuyu Zhang ◽  
Tsz Ling Elaine Tang ◽  
Lucia Mirabella ◽  
Livio Dalloro ◽  
...  
2021 ◽  
Author(s):  
Heng Chi ◽  
Tsz Ling Elaine Tang ◽  
Lucia Mirabella ◽  
Yuyu Zhang ◽  
Glaucio Paulino

2021 ◽  
Author(s):  
Heng Chi ◽  
Tsz Ling Elaine Tang ◽  
Lucia Mirabella ◽  
Yuyu Zhang ◽  
Glaucio Paulino

2019 ◽  
Vol 31 (9) ◽  
pp. 3564-3572 ◽  
Author(s):  
Chi Chen ◽  
Weike Ye ◽  
Yunxing Zuo ◽  
Chen Zheng ◽  
Shyue Ping Ong

2020 ◽  
Vol 142 (8) ◽  
pp. 3814-3822 ◽  
Author(s):  
George S. Fanourgakis ◽  
Konstantinos Gkagkas ◽  
Emmanuel Tylianakis ◽  
George E. Froudakis

2021 ◽  
Vol 10 ◽  
pp. 135-143
Author(s):  
Sai K. Devana ◽  
Akash A. Shah ◽  
Changhee Lee ◽  
Andrew R. Roney ◽  
Mihaela van der Schaar ◽  
...  

2021 ◽  
Vol 11 (24) ◽  
pp. 12044
Author(s):  
Nikos Ath. Kallioras ◽  
Nikos D. Lagaros

Design and manufacturing processes are entering into a new era as novel methods and techniques are constantly introduced. Currently, 3D printing is already established in the production processes of several industries while more are continuously being added. At the same time, topology optimization has become part of the design procedure of various industries, such as automotive and aeronautical. Parametric design has been gaining ground in the architectural design literature in the past years. Generative design is introduced as the contemporary design process that relies on the utilization of algorithms for creating several forms that respect structural and architectural constraints imposed, among others, by the design codes and/or as defined by the designer. In this study, a novel generative design framework labeled as MLGen is presented. MLGen integrates machine learning into the generative design practice. MLGen is able to generate multiple optimized solutions which vary in shape but are equivalent in terms of performance criteria. The output of the proposed framework is exported in a format that can be handled by 3D printers. The ability of MLGen to efficiently handle different problems is validated via testing on several benchmark topology optimization problems frequently employed in the literature.


2021 ◽  
Vol 2 (4) ◽  
pp. 91-99
Author(s):  
Zhouwei Gang ◽  
Qianyin Rao ◽  
Lin Guo ◽  
Lin Xi ◽  
Zezhong Feng ◽  
...  

Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible topology numbers equal to node number multiplied by number of links means it is impossible for humans to optimize. We use Al instead of humans for topology optimization. The AI optimization solution uses an ITU Machine Learning (ML) standard, Breadth-First Search (BFS) greedy algorithm and other mainstream algorithms to solve the problem. It saves a lot of money and human resources, and also hugely improves traffic absorption capacity. The author comes from the team named "No Boundaries". The team attend ITU AI/ML in 5G Challenge and won the Gold champions (1st place).


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