In recent years, reduced-order modeling techniques have proven to be powerful tools for
various problems in circuit simulation. For example, today, reduction techniques are …

A hybrid neural network based on Densely Connected Convolutional Networks (DenseNet),
Convolutional Long Short-Term Memory Neural Network (ConvLSTM), and Deconvolutional …

Purpose To present an open‐source MR simulation framework that facilitates the
incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition …

Simulating forced time-periodic flows in industrial applications presents significant
computational challenges, partly due to the need to overcome costly transients before …

In this paper, we propose a residual-based reduced-order model (ROM) framework that
utilizes available data to increase the ROM accuracy and stability. The available snapshots …

This paper deals with the numerical modeling of flow around and through a porous obstacle
by a reduced order model (ROM) obtained by Galerkin projection of the Navier–Stokes …

In the context of traditional reduced order modeling methods (ROMs), time and parameter
extrapolation tasks remain a formidable challenge. To this end, we propose a hybrid …

In the Reduced Basis approximation of Stokes and Navier-Stokes problems, the Galerkin
projection on the reduced spaces does not necessarily preserved the inf-sup stability even if …

This study demonstrates unsteady incompressible flow over a backward-facing step and a
cylinder section using a finite element scheme for solving the unsteady Navier-Stokes …

This paper deals with the numerical modeling of flow around and through a porous obstacle
by a reduced order model (ROM) obtained by Galerkin projection of the Navier-Stokes …