In this work we present an advanced computational pipeline for the approximation and
prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced …

Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of
computational science out of which actionable outcomes are anticipated. Much of the …

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 …

Renewable energy researchers use computer simulation to aid the design of lithium ion
storage devices. The underlying models contain several physical input parameters that …

Providing a model that achieves a strong predictive performance and is simultaneously
interpretable by humans is one of the most difficult challenges in machine learning research …

The deep active-subspace method is a neural-network based tool for the propagation of
uncertainty through computational models with high-dimensional input spaces. Unlike the …

Bayesian inference for neural networks, or Bayesian deep learning, has the potential to
provide well-calibrated predictions with quantified uncertainty and robustness. However, the …

Over the past decades, the increasing penetration of distributed energy resources (DERs)
has dramatically changed the power load composition in the distribution networks. The …

Modern day engineering problems are ubiquitously characterized by sophisticated computer
codes that map parameters or inputs to an underlying physical process. In other situations …

Active Subspaces is a recently developed concept that identifies essential directions of the
response surface of a model, providing sensitivity metrics known as activity scores. We …