Inverse structural design with generative and probabilistic autoencoders and diffusion models

Traditional structural design is a forward trial-and-error process. Designers need to iterate through different design solutions and conduct structural analysis until the design meets the codes and standards. This study proposes and investigates a generative machine learning (ML) framework for inver...

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Vydané v:	Engineering applications of artificial intelligence Ročník 161; s. 112143
Hlavní autori:	Zhuang, Bozhou, Gallet, Adrien, Smyl, Danny
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.12.2025
Predmet:	Conditional autoencoders Continuous beams Denoising diffusion models Generative artificial intelligence Inverse problem Structural design Tabular data Structural design Generative artificial intelligence Tabular data Continuous beams Conditional autoencoders Denoising diffusion models Inverse problem
ISSN:	0952-1976
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