Skip to main navigation
Skip to search
Skip to main content
Arizona State University Home
Home
Profiles
Departments and Centers
Scholarly Works
Activities
Equipment
Grants
Datasets
Prizes
Search by expertise, name or affiliation
One-shot generation of near-optimal topology through theory-driven machine learning
Ruijin Cang, Hope Yao,
Yi Ren
Mechanical and Aerospace Engineering
Engineering of Matter, Transport and Energy, School for (IAFSE-SEMTE)
Research output
:
Contribution to journal
›
Article
›
peer-review
47
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'One-shot generation of near-optimal topology through theory-driven machine learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering & Materials Science
Supervised learning
100%
Machine learning
92%
Topology
85%
Students
69%
Compliance
55%
Neural networks
38%
Shape optimization
33%
Deep neural networks
30%