We propose a general---ie, independent of the underlying equation---registration method for
parameterized model order reduction. Given the spatial domain Ω⊂R^d and the manifold …

Abstract Models with dominant advection always posed a difficult challenge for projection-
based reduced order modelling. Many methodologies that have recently been proposed are …

Mode-based model-reduction is used to reduce the degrees of freedom of high-dimensional
systems, often by describing the system state by a linear combination of spatial modes …

In the last few years, several methods have been developed to deal with jump singularities
in parametric or stochastic hyperbolic PDEs. They typically use some alignment of the jump …

We present a general—ie, independent of the underlying equation—egistration procedure
for parameterized model order reduction. Given the spatial domain\varOmega ⊂ R^ 2 Ω⊂ R …

When approximating a function that depends on a parameter, one encounters many
practical examples where linear interpolation or linear approximation with respect to the …

This work presents a method for constructing online-efficient reduced models of large-scale
systems governed by parametrized nonlinear scalar conservation laws. The solution …

A robust, low-order POD-based state estimator, also known as an observer, for the
challenging fluid-dynamics test-case of uncertain 2D Boussinesq equations is presented in …

We present methodologies for reduced order modeling of convection dominated flows.
Accordingly, three main problems are addressed. Firstly, an optimal manifold is realized to …

A methodology for designing robust, low-order observers for a class of spectral infinite-
dimensional nonlinear systems is presented. This approach uses the low-dimensional …