Comparative study on manifold learning approaches for parametric topology optimization problem via unsupervised deep learning

DOI10.1007/S11081-023-09806-YzbMATH Open1540.74112MaRDI QIDQ6547730

Haeseong Cho, Seongwoo Cheon

Publication date: 31 May 2024

Published in: Optimization and Engineering (Search for Journal in Brave)

zbMATH Keywords

penalization methodmodel order reductiondeep neural networkconvolutional neural networkiterative/non-iterative network framework

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Topological methods for optimization problems in solid mechanics (74P15) Numerical and other methods in solid mechanics (74S99)

Cites Work

• Title not available (Why is that?)
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• Bi-directional evolutionary topology optimization of continuum structures with one or multiple materials
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• The evolutionary structural optimization method: theoretical aspects.
• Multiscale topology optimization using neural network surrogate models
• Robust topology optimization for multi-material structures under interval uncertainty
• A new level set based multi-material topology optimization method using alternating active-phase algorithm
• Neural networks for topology optimization
• Explicit three dimensional topology optimization via moving morphable void (MMV) approach
• Prediction of aerodynamic flow fields using convolutional neural networks

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