In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

Computer-aided engineering (CAE) is now an essential instrument that aids in engineering
decision-making. Statistical model calibration and validation has recently drawn great …

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

A data–informed approach is presented with the objective of quantifying errors and
uncertainties in the functional forms of turbulence closure models. The approach creates …

Recent work on solving partial differential equations (PDEs) with deep neural networks
(DNNs) is presented. The paper reviews and extends some of these methods while carefully …

Simulation is becoming increasingly important in the development, testing and approval
process in many areas of engineering, ranging from finite element models to highly complex …

This work introduces a novel methodology for the quantification of uncertainties associated
with potential energy surfaces (PESs) computed from first-principles quantum mechanical …

A comprehensive uncertainty quantification framework has been developed for integrating
computational and experimental kinetic data and to identify active sites and reaction …

Model error estimation remains one of the key challenges in uncertainty quantification and
predictive science. For computational models of complex physical systems, model error, also …

Inference of physical parameters from reference data is a well‐studied problem with many
intricacies (inconsistent sets of data due to experimental systematic errors; approximate …