The need for multiple interactive, real-time simulations using different parameter values has
driven the design of fast numerical algorithms with certifiable accuracies. The reduced basis …

Abstract Model order reduction usually consists of two stages: the offline stage and the
online stage. The offline stage is the expensive part that sometimes takes hours till the final …

An adaptive scheme to generate reduced‐order models for parametric nonlinear dynamical
systems is proposed. It aims to automatize the proper orthogonal decomposition (POD) …

We present a subsampling strategy for the offline stage of the Reduced Basis Method. The
approach is aimed at bringing down the considerable offline costs associated with using a …

We present an accelerated greedy strategy for training of projection-based reduced-order
models for parametric steady and unsteady partial differential equations. Our approach …

The task of repeatedly solving parametrized partial differential equations (pPDEs) in
optimization, control, or interactive applications makes it imperative to design highly efficient …

The reduced basis method (RBM) empowers repeated and rapid evaluation of parametrized
partial differential equations through an offline–online decomposition, aka a learning …

In this work, we consider compressible single-phase flow problems in a porous medium
containing a fracture. In the fracture, a nonlinear pressure-velocity relation is prescribed …

The need for accelerating the repeated solving of certain parametrized systems motivates
the development of more efficient reduced order methods. The classical reduced basis …

In this contribution, we present a modelling and simulation framework for parametrised
lithium-ion battery cells. We first derive a continuum model for a rather general intercalation …