We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial
differential equations (PDEs). The method extracts a reduced basis from a collection of high …

During the past decade, reduced order modeling has attracted growing interest in
computational science and engineering. It now plays an important role in delivering high …

A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A
set of reduced basis functions are extracted from a collection of high-fidelity solutions via a …

This article presents two new non-intrusive reduced order models based upon proper
orthogonal decomposition (POD) for solving the Navier–Stokes equations. The novelty of …

Abstract Model reduction is a method that maps full-order conservation equations into lower-
order subspaces or establish a data-driven surrogate model to reduce the complexity of the …

A greedy nonintrusive reduced order method (ROM) is proposed for parameterized time-
dependent problems with an emphasis on problems in fluid dynamics. The nonintrusive …

Parameter-dependent models arise in many contexts such as uncertainty quantification,
sensitivity analysis, inverse problems or optimization. Parametric or uncertainty analyses …

In this work, we propose a framework that constructs reduced‐order models for nonlinear
structural mechanics in a nonintrusive fashion and can handle large‐scale simulations …

We provide first the functional analysis background required for reducedorder modeling and
present the underlying concepts of reduced basis model reduction. The projection-based …