Computational Mathematics, Algorithms, and Data Processing

• Autore: Mortari Daniele
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (172 p.)
• Soggetto topico: Mathematics and Science; Research and information: general
• Soggetto non controllato: approximation; Approximation Theory; clustering; constraints; Darcy-Forchheimer model; deep learning; Deep Neural Nets; directional statistics; embedded constraints; finite element method; flow in porous media; generalized multiscale finite element method; heterogeneous media; interpolation; inverse difference; linear multistep methods; mixed generalized multiscale finite element method; multiscale basis functions; multiscale method; multiscale model reduction; native spaces; nonlinear equation; Obrechkoff schemes; polynomial chaos; radial basis functions; ReLU Networks; Rogers-Szegő; second order initial value problems; state estimation; surface modeling; Szegő polynomials; Thiele-like rational interpolation continued fractions with parameters; trigonometrically fitted; truncated function; two-dimensional domain; unattainable point; virtual point
• Persona (resp. second.): EfendievYalchin; HaninBoris; MortariDaniele
• Sommario/riassunto: "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.
• Titolo autorizzato: Computational Mathematics, Algorithms, and Data Processing
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557304403321
• Lo trovi qui: Univ. Federico II