Results 41 to 50 of about 1,054,196 (331)
Remote Sensing, 2023 The inclusion of physiographic and atmospheric influences is critical for spatial modeling of orographic precipitation in complex terrains. However, attempts to incorporate cloud cover frequency (CCF) data when interpolating precipitation are limited ...
Karam Alsafadi +6 more
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On the Regularity of Multivariate Hermite Interpolation
Journal of Approximation Theory, 2000 In this paper, former joint studies of Gevorgian, Sahakian and the author concerning multivariate Hermite interpolation are continued. Consider \(n_1,\dots,n_s\) to be a set of multiplicities, and let \(n\) denote the maximal total degree of the interpolating polynomials. Then the scheme \({\mathcal N}=\{n_1, \dots, n_s;n\}\) is said to be independent,
Hakop Hakopian
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Guaranteed passive parameterized admittance-based macromodeling [PDF]
, 2010 We propose a novel parametric macromodeling technique for admittance and impedance input-output representations parameterized by design variables such as geometrical layout or substrate features. It is able to build accurate multivariate macromodels that
Dhaene, Tom +2 more
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Sparse Interpolation in Terms of Multivariate Chebyshev Polynomials [PDF]
Foundations of Computational Mathematics, 2020 Sparse interpolation refers to the exact recovery of a function as a short linear combination of basis functions from a limited number of evaluations.
E. Hubert, M. Singer
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General multivariate arctangent function activated neural network approximations
Journal of Numerical Analysis and Approximation Theory, 2022 Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N}\), \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
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Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset
Scientific Data, 2020 CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica.
I. Harris +3 more
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An estimate for multivariate interpolation
Journal of Approximation Theory, 1985 An estimate of the \(L^ p=L^ p(R^ n)\) norm of a function is given in terms of its values on a (not necessarily regular) grid of points and the \(L^ p\) norms of its k-th order derivatives, \(kp>n\). The result is useful in obtaining estimates for multivariate interpolation schemes; this is illustrated by an example involving generalized splines.
Madych, W.R, Potter, E.H
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Approximations by multivariate sublinear and Max-product operators under convexity
Demonstratio Mathematica, 2018 Here we search quantitatively under convexity the approximation of multivariate function by general multivariate positive sublinear operators with applications to multivariate Max-product operators.
Anastassiou George A.
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On Multivariate Interpolation [PDF]
Studies in Applied Mathematics, 2005 A new approach to interpolation theory for functions of several variables is proposed. We develop a multivariate divided difference calculus based on the theory of noncommutative quasi‐determinants. In addition, intriguing explicit formulae that connect the classical finite difference interpolation coefficients for univariate curves with multivariate ...
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Canonical Sets of Best L1-Approximation
Journal of Function Spaces and Applications, 2012 In mathematics, the term approximation usually means either interpolation on a point set or approximation with respect to a given distance. There is a concept, which joins the two approaches together, and this is the concept of characterization of the ...
Dimiter Dryanov, Petar Petrov
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