Predictions from science and engineering models depend on several input parameters.
Global sensitivity analysis quantifies the importance of each input parameter, which can lead …

One of today's most propitious immersive technologies is virtual reality (VR). This term is
colloquially associated with headsets that transport users to a bespoke, built-for-purpose …

Random feature methods have been successful in various machine learning tasks, are easy
to compute, and come with theoretical accuracy bounds. They serve as an alternative …

Polynomial dimensional decomposition (PDD) is a surrogate method originated from the
ANOVA (analysis of variance) decomposition, and has shown powerful performance in …

This study presents a novel two-stage machine learning algorithm that identifies dominant
dimensionless numbers for critical heat flux (CHF) prediction, circumventing the limitations of …

We present the results of the first application in the naval architecture field of a methodology
based on active subspaces properties for parameter space reduction. The physical problem …

In this chapter we introduce a combined parameter and model reduction methodology and
present its application to the efficient numerical estimation of a pressure drop in a set of …

Large scale dynamical systems (eg many nonlinear coupled differential equations) can often
be summarized in terms of only a few state variables (a few equations), a trait that reduces …

Inexpensive surrogates are useful for reducing the cost of science and engineering studies
involving large-scale, complex computational models with many input parameters. A ridge …

In this work we present an integrated computational pipeline involving several model order
reduction techniques for industrial and applied mathematics, as emerging technology for …