A new hierarchical Bayesian framework (HBM) is proposed for identification of Gaussian
processes or fields, which are usually used for simulating uncertainty in temporal variability …

Gaussian random fields (GFs) are fundamental tools in spatial modeling and can be
represented flexibly and efficiently as solutions to stochastic partial differential equations …

Many inverse problems focus on recovering a quantity of interest that is a priori known to
exhibit either discontinuous or smooth behavior. Within the Bayesian approach to inverse …

Inverse problems arise when the quantity of interest needs to be reconstructed from its noisy
indirect measurements. Numerous examples of inverse problems include remote sensing …

We investigate solution methods for large-scale inverse problems governed by partial
differential equations (PDEs) via Bayesian inference. The Bayesian framework provides a …

This thesis presents a collection of original contributions pertaining to the subjects of
reliability-based design optimization (RBDO) and model updating of civil engineering …

Dike failure poses a great risk to areas around rivers and coasts, one of the ways this can
happen is piping. A difficulty in the prediction of piping is characterising the subsoil under …

Bayesian inference and rare event simulation increase in complexity when model
parameters are represented through random fields. We propose two algorithms that are …

This thesis presents a collection of original contributions pertaining to the subjects of
reliabilitybased design optimization (RBDO) and model updating of civil engineering …