Generative Shape Optimization
Generative Shape Optimization is a computational design methodology that belongs to the class of generative modeling techniques, distinguished by its integration of performance-driven objectives and iterative morphological refinement to autonomously synthesize optimal geometric configurations in response to defined constraints and functional criteria.
13
Publications
491
Citations
34
Authors
13
Institutions
Publications per year
2017–2026
10
Parents
13
1–5 of 13
34
2
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1–5 of 34
13
University of Maryland, College Park
College Park, United States
2 h-index
4 publications
337 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
8 publications
108 citations
Schlaich Bergermann und Partner (Germany)
Stuttgart, Germany
1 h-index
1 publications
35 citations
Institute for Advanced Study
Berlin, Germany
1 h-index
1 publications
11 citations

Southeast University
Nanjing, China
1 h-index
5 publications
180 citations
Technical University of Munich
Munich, Germany
1 h-index
1 publications
11 citations
Oregon State University
Corvallis, United States
1 h-index
1 publications
11 citations
Instituto Politécnico de Lisboa
Lisbon, Portugal
1 h-index
3 publications
117 citations
1–8 of 13
1 publications · 10 citations
2
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1 publications · 102 citations
1
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1 publications · 39 citations
1
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1 publications · 81 citations
1
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1 publications · 17 citations
1
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1–5