This paper proposes a supervised machine learning framework for the non-intrusive model
order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state …

Reduced order models (ROMs) have achieved a lot of success in reducing the
computational cost of traditional numerical methods across many disciplines. For convection …

Projection-based reduced order models rely on offline–online model decomposition, where
the data-based energetic spatial basis is used in the expensive offline stage to obtain …

For over a century, reduced order models (ROMs) have been a fundamental discipline of
theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr …

Galerkin reduced order models (ROMs), eg, based on proper orthogonal decomposition
(POD) or reduced basis methods, have achieved significant success in the numerical …

Abstract We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced
Order Model (ROM) for an implementation of the Leray model that combines a two-step …

Friedrichs' systems (FS) are symmetric positive linear systems of first-order partial differential
equations (PDEs), which provide a unified framework for describing various elliptic …

For incompressible flow models, the pressure term serves as a Lagrange multiplier to
ensure that the incompressibility constraint is satisfied. In engineering applications, the …

Abstract Galerkin and Petrov–Galerkin projection-based reduced-order models (ROMs) of
transient partial differential equations are typically obtained by performing a dimension …

In this paper, we resolve several long-standing issues dealing with optimal pointwise in time
error bounds for proper orthogonal decomposition (POD) reduced order modeling of the …