Original paper
Multiscale topology optimization using neural network surrogate models
Volume: 346, Pages: 1118 - 1135
Published: Apr 1, 2019
Abstract
We are concerned with optimization of macroscale elastic structures that are designed utilizing spatially varying microscale metamaterials. The macroscale optimization is accomplished using gradient-based nonlinear topological optimization. But instead of using density as the optimization decision variable, the decision variables are the multiple parameters that define the local microscale metamaterial. This is accomplished using single layer...
Paper Details
Title
Multiscale topology optimization using neural network surrogate models
Published Date
Apr 1, 2019
Volume
346
Pages
1118 - 1135
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History