A POD-Galerkin reduced-order model for two-dimensional Rayleigh-Bénard convection with viscoelastic fluid

Model Reduction by Generalized Falk Method for Efficient Field-Circuit Simulations

“…We have written a MATLAB code to automatically generate element templates 13 of the 1-D field networks 14 in Fig. 26 (Lanczos transformation) and Fig.…”

“…Reduced-order models have been developed in many areas of engineering, such as circuits (e.g., [1][2][3][4]), power systems (e.g., [5][6][7]), electromagnetics (e.g., [8][9][10]), fluid mechanics (e.g., [11][12][13]), nonlinear structural mechanics and earthquake engineering (e.g., [14]), nonlinear hydraulic fracturing problems (e.g., [15]), etc., to cite a few. Deep-learning artificial neural networks have been introduced to build on more traditional model-order reduction methods, such as the Proper Orthogonal Decomposition (POD), 1 to increase computational efficiency [16][17][18][19][20][21].…”

Brownian motion and thermophoresis effects on MHD flow of viscoelastic fluid over stretching/shrinking sheet in the presence of thermal radiation and chemical reaction

“…In very important applications, viscoelastic fluids are common. [1][2][3] In recent years, magnetohydrodynamic (MHD) research work in the field of natural sciences and engineering has been progressing significantly since Hartmann 4 pioneered work in the flow of liquid metal pipelines through the influences of an external magnetic field. Many researchers have recently been working on viscoelastic fluid MHD flow.…”

Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling

“…Nevertheless, the cost associated with the development and application of the predictive models is significant, rendering the multi-parametric investigation a time-and resourceconsuming task. The answer to this problem is given by data-mining in the form of the popular Proper Orthogonal Decomposition (POD) method (Sipp et al, 2020; Wang et al, 2020;Li et al, 2019;Hijazi et al, 2020;Dey and Dhar, 2020), that has led to model order reduction strategies by discovering low-order descriptions of the available data, i.e. an orthogonal basis of the subspace containing the data.…”