This paper introduces an innovative data-driven approach to uncertainty quantification (UQ)
in complex engineering designs based on polynomial chaos expansion (PCE) and least …

This paper introduces a novel hybrid method for uncertainty quantification (UQ) combining
the benefits of polynomial chaos expansion (PCE) and Gaussian process regression (GPR) …

Areas of computational mechanics such as uncertainty quantification and optimization
usually involve repeated evaluation of numerical models that represent the behavior of …

We consider the effect of multiple stochastic parameters on the time-average quantities of
chaotic systems. We employ the recently proposed sensitivity-enhanced generalized …

Surrogate models provide a quick-to-evaluate approximation to complex computational
models and are essential for multi-query problems like design optimisation. The inputs of …

This letter proposes a novel model order reduction (MOR) approach leveraging frequency-
domain proper orthogonal decomposition (POD) for partial element equivalent circuit …

This paper presents a method for performing Uncertainty Quantification in high-dimensional
uncertain spaces by combining arbitrary polynomial chaos with a recently proposed scheme …