Surrogate models are popular tool to approximate the functional relationship of expensive
simulation models in multiple scientific and engineering disciplines. Successful use of …

Uncertainty quantification has become a hot topic in computational sciences in the last
decade. Indeed computer models (aka simulators) are becoming more and more complex …

Assessing the failure probability of complex structure is a difficult task in presence of various
uncertainties. In this paper, a new adaptive approach is developed for reliability analysis by …

In the context of uncertainty analysis, Polynomial chaos expansion (PCE) has been proven
to be a powerful tool for developing meta-models in a wide range of applications, especially …

Applied soft computing has been widely used to predict material properties, optimal mixture,
and failure modes. This is challenging, especially for the highly nonlinear behavior of brittle …

Uncertainty quantification (UQ) and global sensitivity analysis (GSA) of dynamic
characteristics of complex systems subjected to uncertainty are jointly investigated in this …

Thanks to their versatility, ease of deployment, and high performance, surrogate models
have become staple tools in the arsenal of uncertainty quantification (UQ). From local …

For wave energy converters (WECs), wind turbines, etc., estimation of response extremes
over a selected exposure time is important during design. Sources of uncertainty arising …

Reliability analysis requires a large number of original model evaluations, especially for
high-nonlinear and high-dimensional problems. This could be computationally expensive …

Dynamic systems modeled by computationally intensive numerical models with time-
dependent output are common in engineering. Efficient uncertainty propagation of such …