In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

This book provides a basic introduction to reduced basis (RB) methods for problems
involving the repeated solution of partial differential equations (PDEs) arising from …

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

Abstract Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …

In this work a stabilised and reduced Galerkin projection of the incompressible unsteady
Navier–Stokes equations for moderate Reynolds number is presented. The full-order model …

This work presents a model reduction approach for problems with coherent structures that
propagate over time, such as convection-dominated flows and wave-type phenomena …

We propose a data-driven Model Order Reduction (MOR) technique, based on Artificial
Neural Networks (ANNs), applicable to dynamical systems arising from Ordinary Differential …

Parametric model order reduction using reduced basis methods can be an effective tool for
obtaining quickly solvable reduced order models of parametrized partial differential equation …