We consider in this paper the problem of building a fast-running approximation− also called
surrogate model− of a complex computer code. The co-kriging based surrogate model is a …

In this paper, a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is
proposed for reliability analysis. Instead of leveraging on additional techniques to reduce the …

Developing surrogate models for costly mathematical models representing physical systems
is challenging since it is typically not possible to generate large training data sets, ie to …

Stochastic simulators are non-deterministic computer models which provide a different
response each time they are run, even when the input parameters are held at fixed values …

We present a Bayesian tomography framework operating with prior-knowledge-based
parametrization that is accelerated by surrogate models. Standard high-fidelity forward …

A novel approach for the construction of polynomial chaos expansion (PCE) is proposed to
facilitate high-dimensional uncertainty quantification (UQ). The current PCE techniques are …

The mechanical properties of 3D printed materials produced with additive manufacturing
depend on the printing process, which is controlled by several tuning parameters. This …

In this chapter, we discuss recent work on learning sparse approximations to high-
dimensional functions on data, where the target functions may be scalar-,'vector-or even …

In the context of Bayesian inversion, we consider sequential Monte Carlo (SMC) methods
that provide an approximation of the posterior probability density function and the evidence …

This work develops a novel basis-adaptive method for constructing anisotropic polynomial
chaos expansions of multidimensional (vector-valued, multi-output) model responses. The …