Machine-learning-assisted topology optimization for highly efficient thermal emitter design

2019 ◽  
Author(s):  
Zhaxylyk A. Kudyshev ◽  
Alexander V. Kildishev ◽  
Vladimir M. Shalaev ◽  
Alexandra Boltasseva
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Highly Efficient ◽  
Thermal Emitter

scholarly journals MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization

2021 ◽  
Vol 11 (24) ◽  
pp. 12044
Author(s):  
Nikos Ath. Kallioras ◽  
Nikos D. Lagaros
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Architectural Design ◽  
Parametric Design ◽  
Optimization Problems ◽  
Design Procedure ◽  
Performance Criteria ◽  
Design Framework ◽  
Generative Design ◽  
New Era

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.

scholarly journals A highly efficient CMOS nanoplasmonic crystal enhanced slow-wave thermal emitter improves infrared gas-sensing devices

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Andreas Pusch ◽  
Andrea De Luca ◽  
Sang S. Oh ◽  
Sebastian Wuestner ◽  
Tyler Roschuk ◽  
...  
Keyword(s):  
Slow Wave ◽  
Gas Sensing ◽  
Highly Efficient ◽  
Thermal Emitter

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 ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Universal Machine

scholarly journals Applying machine learning in network topology optimization

2021 ◽  
Vol 2 (4) ◽  
pp. 91-99
Author(s):  
Zhouwei Gang ◽  
Qianyin Rao ◽  
Lin Guo ◽  
Lin Xi ◽  
Zezhong Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Human Resources ◽  
Network Topology ◽  
Absorption Capacity ◽  
Transmission Network ◽  
Breadth First Search ◽  
Communication Services ◽  
Node Number ◽  
Network Topology Optimization

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).

Machine Learning-Driven Real-Time Topology Optimization Under Moving Morphable Component-Based Framework

2018 ◽  
Vol 86 (1) ◽  
Author(s):  
Xin Lei ◽  
Chang Liu ◽  
Zongliang Du ◽  
Weisheng Zhang ◽  
Xu Guo
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Real Time ◽  
Training Data ◽  
Design Parameters ◽  
Structural Topology ◽  
Training Set ◽  
K Nearest Neighbors ◽  
External Stimuli ◽  
External Loads

In the present work, it is intended to discuss how to achieve real-time structural topology optimization (i.e., obtaining the optimized distribution of a certain amount of material in a prescribed design domain almost instantaneously once the objective/constraint functions and external stimuli/boundary conditions are specified), an ultimate dream pursued by engineers in various disciplines, using machine learning (ML) techniques. To this end, the so-called moving morphable component (MMC)-based explicit framework for topology optimization is adopted for generating training set and supported vector regression (SVR) as well as K-nearest-neighbors (KNN) ML models are employed to establish the mapping between the design parameters characterizing the layout/topology of an optimized structure and the external load. Compared with existing approaches, the proposed approach can not only reduce the training data and the dimension of parameter space substantially, but also has the potential of establishing engineering intuitions on optimized structures corresponding to various external loads through the learning process. Numerical examples provided demonstrate the effectiveness and advantages of the proposed approach.

Broadband and Highly Efficient Integrated Polarization Rotator Designed by Topology Optimization

2021 ◽  
Author(s):  
Rômulo Aparecido de Paula Junior ◽  
Yesica Bustamante ◽  
Ivan Aldaya
Keyword(s):  
Topology Optimization ◽  
Polarization Rotator ◽  
Highly Efficient
Sign in / Sign up

Export Citation Format

Share Document