Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

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 serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

The fracture energy is a substantial material property that measures the ability of materials to
resist crack growth. The reinforcement of the epoxy polymers by nanosize fillers improves …

Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …

In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

We discuss the arbitrary polynomial chaos (aPC), which has been subject of research in a
few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC …

In this paper, sensitivity analysis has been applied to identify the key input parameters
influencing the energy conversion factor (ECF) of flexoelectric materials. The governing …

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 …

Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …