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Stochastic spectral likelihood embedding for the calibration of heat transfer models
(2021)Other Conference Item -
An adaptive algorithm based on spectral likelihood expansion for efficient Bayesian calibration
(2019)Other Conference Item -
Surrogate models for uncertainty quantification and design optimization
(2019)Nowadays computational models are used in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. Also known as simulators, they allow the engineer to assess the performance of a system in-silico, and then optimize its design or operating. Realistic models (e.g. finite element models) usually feature tens of parameters and are costly to run, even when taking full advantage ...Other Conference Item -
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A new approach for Bayesian model calibration using stochastic spectral embedding
(2019)Other Conference Item -
Surrogate-based Bayesian Inversion for the Model Calibration of Fire Insulation Panels
(2018)We outline an approach for the calibration of a finite element model (FEM) by using a full Bayesian inference approach. The considered FEM describes the heat transfer in insulation when exposed to fire, it uses so-called temperature dependent effective material parameters. These parameters are required for applying the improved component additive method to determine the fire resistance of e.g. timber frame assemblies. After setting up the ...Other Conference Item