Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate

no code implementations • 21 Feb 2022 • Sascha Ranftl, Malte Rolf-Pissarczyk, Gloria Wolkerstorfer, Antonio Pepe, Jan Egger, Wolfgang von der Linden, Gerhard A. Holzapfel

Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis.

Uncertainty Quantification