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Computational Mathematics, Algorithms, and Data Processing
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“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.
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Keywords
- 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
- Mathematics & science
- 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
- Reference, information & interdisciplinary subjects
- ReLU Networks
- Research & information: general
- 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
Links
DOI: 10.3390/books978-3-03943-592-0Editions