Neural Generative Design
Neural Generative Design is a computational design methodology that synthesizes generative models with neural architectures to autonomously produce novel forms or structures through learned implicit design principles and data-driven exploration.
8
Publications
334
Citations
41
Authors
11
Institutions
Publications per year
2017–2026
8
Parents
41
3
h-index
3
h-index
2
h-index
University of South Carolina, United States
2 publications · 51 citations
2
h-index
2
h-index
1–5 of 41
11
University of South Carolina
Columbia, United States
3 h-index
20 publications
480 citations

Pennsylvania State University
State College, United States
1 h-index
6 publications
66 citations
Korea University
Tokyo, Japan
1 h-index
3 publications
126 citations

Beijing Forestry University
Beijing, China
1 h-index
9 publications
414 citations

University of Milano-Bicocca
Milan, Italy
1 h-index
1 publications
142 citations

University of Southern Maine
Portland, United States
1 h-index
1 publications
28 citations
ETH Zurich
Zurich, Switzerland
1 h-index
4 publications
568 citations

Harbin Institute of Technology
Harbin, China
1 h-index
5 publications
115 citations
1–8 of 11
AO · 1 publications · 23 citations
1
h-index
1 publications · 46 citations
1
h-index
1 publications · 28 citations
1
h-index
1 publications · 19 citations
1
h-index
1 publications · 42 citations
1
h-index
1–5