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The consistency problem, which pertains to constraints, interpolation, and functional interpolation, is comprehensively addressed in. [3] This includes the consistency challenges associated with boundary conditions that involve shear and mixed derivatives. The univariate version of TFC can be expressed in one of the following two forms:
The Theory of Functional Connections (TFC) is a mathematical framework specifically developed for functional interpolation.Given any interpolant that satisfies a set of constraints, TFC derives a functional that represents the entire family of interpolants satisfying those constraints, including those that are discontinuous or partially defined.
Radial basis function (RBF) interpolation is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured data, possibly in high-dimensional spaces. The interpolant takes the form of a weighted sum of radial basis functions .
Multivariate interpolation — the function being interpolated depends on more than one variable Barnes interpolation — method for two-dimensional functions using Gaussians common in meteorology; Coons surface — combination of linear interpolation and bilinear interpolation; Lanczos resampling — based on convolution with a sinc function
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In numerical analysis and approximation theory, basis functions are also called blending functions, because of their use in interpolation: In this application, a mixture of the basis functions provides an interpolating function (with the "blend" depending on the evaluation of the basis functions at the data points).
Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, inner product, norm, or topology) and the linear functions defined on these spaces and suitably respecting these structures.
A radial function is a function : [,).When paired with a norm on a vector space ‖ ‖: [,), a function of the form = (‖ ‖) is said to be a radial kernel centered at .A radial function and the associated radial kernels are said to be radial basis functions if, for any finite set of nodes {} =, all of the following conditions are true: