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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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 161; S. 112143
Hauptverfasser:	Zhuang, Bozhou, Gallet, Adrien, Smyl, Danny
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.12.2025
Schlagworte:	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
Online-Zugang:	Volltext
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