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AuthorTrent, Stephen
AuthorSassi, Sadok
AuthorM. Shadi, Mohamed
AuthorRenno, Jamil
Available date2022-05-11T08:16:29Z
Publication Date2022-05-08
URIhttp://hdl.handle.net/10576/30833
AbstractWe use cGANs to solve computational mechanics. We model a structure using FEA and generate simulations results (deflection and stress). We then transform the FEA results into images where the colour channels correspond to the loading, material and geometric properties of the structure under study. We then use the images as input to the cGAN which is trained to infer deflections from forces, stresses from deflections and stresses from forces. The submission includes the data and the code used in this work.
SponsorThe authors gratefully acknowledge the financial support provided by the Qatar National Research Fund through the National Priorities Research Program under grant number NPRP 11S-1220-170112 and Qatar University Internal Grant QUCG-CENG-19/20-6.
Languageen
SubjectcGAN
convolutional neural networks
computational mechanics
TitleUsing Conditional Generative Adversarial Networks in Computational Mechanics -- Code
TypeDataset
dc.accessType Open Access


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