Reprint

# Computational Mathematics, Algorithms, and Data Processing

Edited by

December 2020

172 pages

- ISBN978-3-03943-591-3 (Hardback)
- ISBN978-3-03943-592-0 (PDF)

This book is a reprint of the Special Issue Computational Mathematics, Algorithms, and Data Processing that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

“Computational Mathematics, Algorithms, and Data Processing” of MDPI consists of articles on new mathematical tools and numerical methods for computational problems. Topics covered include: numerical stability, interpolation, approximation, complexity, numerical linear algebra, differential equations (ordinary, partial), optimization, integral equations, systems of nonlinear equations, compression or distillation, and active learning.

Format

- Hardback

License and Copyright

© 2021 by the authors; CC BY-NC-ND license

Keywords

interpolation; constraints; embedded constraints; generalized multiscale finite element method; multiscale model reduction; deep learning; Deep Neural Nets; ReLU Networks; Approximation Theory; radial basis functions; native spaces; truncated function; interpolation; approximation; surface modeling; second order initial value problems; linear multistep methods; Obrechkoff schemes; trigonometrically fitted; Darcy-Forchheimer model; flow in porous media; nonlinear equation; heterogeneous media; finite element method; multiscale method; mixed generalized multiscale finite element method; multiscale basis functions; two-dimensional domain; Thiele-like rational interpolation continued fractions with parameters; unattainable point; inverse difference; virtual point; polynomial chaos; Szegő polynomials; directional statistics; Rogers-Szegő; state estimation; generalized multiscale finite element method; multiscale model reduction; clustering; deep learning