\texttt{DL4TO}: a deep learning library for sample-efficient topology optimization

DOI10.1007/978-3-031-38271-0_54OpenAlexW4385435157MaRDI QIDQ6138163

Henrik Harms, Sören Dittmer, David Erzmann, Peter Maass

Publication date: 16 January 2024

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-031-38271-0_54

zbMATH Keywords

three-dimensional topology optimizationneural network integrationPython software library

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) Software, source code, etc. for problems pertaining to mechanics of deformable solids (74-04)

Cites Work

• Unnamed Item
• Generating optimal topologies in structural design using a homogenization method
• Universal machine learning for topology optimization
• Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design
• TONR: an exploration for a novel way combining neural network with topology optimization
• Neural networks for topology optimization
• Topology optimization of acoustic–structure interaction problems using a mixed finite element formulation
• Topology optimization of fluids in Stokes flow

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