Optimization-aware Generation
Optimization-Aware Generation is a computational synthesis methodology that integrates optimization objectives and constraints directly into the generative process to ensure that generated artifacts are inherently aligned with performance, efficiency, or quality criteria defined by the optimization framework.
12
Publications
742
Citations
39
Authors
20
Institutions
Publications per year
2017–2026
8
Parents
12
Holger H. Hoos · Communications of the ACM · 2012 · 226 citations · Full text
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
Lin Long, Rui Wang, Ruixuan Xiao et al. · 2024 · 79 citations · Full text
Arindam Paul, Wei‐keng Liao, Alok Choudhary et al. · Computational Materials Science · 2019 · 48 citations · Full text
Architecture, Manufacturing Engineering, Mechanical Engineering +16
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Norwegian University of Science and Technology
Trondheim, Norway
1 h-index
1 publications
43 citations
National University of Singapore
Singapore, Singapore
1 h-index
1 publications
37 citations
University of Denver
Denver, United States
1 h-index
2 publications
90 citations

China Three Gorges University
Yichang, China
1 h-index
4 publications
164 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
2 publications
72 citations
Tianjin University
Tianjin, China
1 h-index
1 publications
41 citations

The University of Melbourne
Melbourne, Australia
1 h-index
2 publications
28 citations
Northwestern University
Evanston, United States
1 h-index
4 publications
192 citations
1–8 of 20
1 publications · 226 citations
1
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1 publications · 121 citations
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1 publications · 41 citations
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1 publications · 14 citations
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1 publications · 43 citations
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