On generating parametrised structural data using conditional generative adversarial networks. (arXiv:2203.01641v1 [cs.LG])

A powerful approach, and one of the most common ones in structural health
monitoring (SHM), is to use data-driven models to make predictions and
inferences about structures and their condition. Such methods almost
exclusively rely on the quality of the data. Within the SHM discipline, data do
not always suffice to build models with satisfactory accuracy for given tasks.
Even worse, data may be completely missing from one's dataset, regarding the
behaviour of a structure under different environmental conditions. In …

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