DL-MSTO+: a deep learning-based multi-scale topology optimization framework via positive definiteness ensured material representation network

DOI10.1016/j.cma.2023.116276MaRDI QIDQ6096498

Seungjae Min, Minsik Seo

Publication date: 12 September 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

neural networkshomogenizationtopology optimizationmulti-scaledeep learning

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Computer science (68-XX)

Cites Work

• Unnamed Item
• On projection methods, convergence and robust formulations in topology optimization
• Generating optimal topologies in structural design using a homogenization method
• A variational approach to the theory of the elastic behaviour of multiphase materials
• Materials with prescribed constitutive parameters: An inverse homogenization problem
• Concurrent two-scale topological design of multiple unit cells and structure using combined velocity field level set and density model
• Deep generative modeling for mechanistic-based learning and design of metamaterial systems
• Concurrent multi-scale and multi-material topological optimization of vibro-acoustic structures
• Topology optimization of fluid-structure-interaction problems in poroelasticity
• Nonlinear structural design using multiscale topology optimization. Part I: Static formulation
• DOLFIN
• A review of homogenization and topology optimization I—homogenization theory for media with periodic structure