spectral approximation

spectral approximation

[‚spek·trəl ə‚prȧk·sə′mā·shən]
(mathematics)
A numerical approximation of a function of two or more variables that involves the expansion of the function into a generalized Fourier series, followed by computation of the Fourier coefficients.
References in periodicals archive ?
HUANG, A time-space collocation spectral approximation for a class of time fractional differential equations, Int.
The frequently studied Gegenbauer reconstruction method has been shown to alleviate the effects of the Gibbs phenomenon while restoring the exponential accuracy of the spectral approximation.
Swarztrauber, On the Spectral Approximation of Discrete Scalar and Vector Functions on a Sphere, Siam J.
Tadmor, Super Viscosity and Spectral Approximations of Nonlinear Conservation Laws, Numerical Methods for Fluid Dynamics IV, M.
CHATELIN, Spectral Approximation of Linear Operators, Academic Press, New York, 1983.
OSBORN, Spectral approximation for compact operators, Math.
In this paper we present an extension of the spectral approximation theory for non-compact operators in Hilbert spaces.
Almost at the same time, Lebaud [11] analyzed a similar problem posed on two-dimensional domains by using isoparametric finite elements methods in the framework of the classical spectral approximation theory; see [1].
By using the abstract spectral approximation theory, they proved optimal order error estimates for the eigenfunctions and a double order for eigenvalues.
finite differences, DtN maps, anisotropy, spectral approximation
We also showed that for wave problems, the propagative modes require a spectral approximation on the negative real axis where the poles are located, similar to the isotropic case.
The topics include a model-based frequentist design for univariate and multivariate geostatistics, spatial sampling design by means of spectral approximations to the error process, accounting for design in the analysis of spatial data, spatial design for knot selection in knot-based dimension reduction models, space-time adaptive sampling and data transformations, adaptive sampling design for spatio-temporal prediction, and active learning for monitoring network optimization.
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