The main task of structural reliability analysis is to estimate failure probability of a studied
structure taking randomness of input variables into account. To consider structural behavior …

Polynomial chaos expansions (PCE) have seen widespread use in the context of uncertainty
quantification. However, their application to structural reliability problems has been hindered …

Machine learning-based reliability analysis methods have shown great advancements for
their computational efficiency and accuracy. Recently, many efficient learning strategies …

Global sensitivity analysis is now established as a powerful approach for determining the
key random input parameters that drive the uncertainty of model output predictions. Yet the …

PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models
such as those used in the earth, environmental and engineering sciences. Easy to use and …

Hybrid Simulation is a dynamic response simulation paradigm that merges physical
experiments and computational models into a hybrid model. In earthquake engineering, it is …

The surrogate model is an essential part of modern design optimization and exploration. In
some cases, exploration of design space in multi-objective problems is important to reveal …

We propose a non-intrusive stochastic model reduction method for polynomial chaos
representation of acoustic problems using proper orthogonal decomposition. The random …

In the next decades, due to the cost of fuel and the increasing number of aircraft flying
everyday, the world of aviation will cope with more stringent environmental constraints and …

In surrogate modeling, polynomial chaos expansion (PCE) is popularly utilized to represent
the random model responses, which are computationally expensive and usually obtained by …