Computational Mathematics, Algorithms, and Data Processing

• Autore: Mortari Daniele
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 electronic resource (172 p.)
• Soggetto topico: Research & information: general; Mathematics & science
• Soggetto non controllato: 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; 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; clustering
• 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