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

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Recent applications of machine learning, in particular deep learning, motivate the need to
address the generalizability of the statistical inference approaches in physical sciences. In …

The Gauss–Newton with approximated tensors (GNAT) method is a nonlinear model-
reduction method that operates on fully discretized computational models. It achieves …

A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete
non‐linear static or dynamic computational model in view of enabling its processing in real …

In this paper we consider (hierarchical, Lagrange) reduced basis approximation and a
posteriori error estimation for linear functional outputs of affinely parametrized elliptic …

The computational efficiency of a typical, projection‐based, nonlinear model reduction
method hinges on the efficient approximation, for explicit computations, of the scalar …

DURING the last two decades, giant strides have been achieved in many aspects of
computational engineering and sciences. Higher-order mathematical models, better …