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Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement. [PDF]
NPJ Comput MaterJiang Y +5 more
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Unique Solution for the Estimation of the Plucker Coordinates Using Radial Basis Functions
, 2012 Pedro Miraldo
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Acta Numerica, 2000
Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their ...
M. Buhmann
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Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their ...
M. Buhmann
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, 2017
In this chapter the RBF mathematical concepts are exposed considering firstly the interpolation problem with the RBF function defined by known values at source points; a first hands-on example is provided showing how RBF work. Further topics of RBF theory are then introduced considering the differentiation of RBF, the fitting of an RBF with known ...
M. Biancolini
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In this chapter the RBF mathematical concepts are exposed considering firstly the interpolation problem with the RBF function defined by known values at source points; a first hands-on example is provided showing how RBF work. Further topics of RBF theory are then introduced considering the differentiation of RBF, the fitting of an RBF with known ...
M. Biancolini
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Handbook of Neural Network Signal Processing, 2018
This chapter considers radial basis function (RBF) networks. A RBF network can be described as a parametrized model used to approximate an arbitrary function by means of a linear combination of basic functions. RBF networks belong to the class of kernel function networks where the inputs to the model are passed through kernel functions which limit the ...
A. Back
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This chapter considers radial basis function (RBF) networks. A RBF network can be described as a parametrized model used to approximate an arbitrary function by means of a linear combination of basic functions. RBF networks belong to the class of kernel function networks where the inputs to the model are passed through kernel functions which limit the ...
A. Back
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, 2014
The traditional basis functions in numerical PDEs are mostly coordinate functions, such as polynomial and trigonometric functions, which are computationally expensive in dealing with high dimensional problems due to their dependency on geometric complexity.
Wen Chen, Zhuojia Fu, Chih-Hao Chen
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The traditional basis functions in numerical PDEs are mostly coordinate functions, such as polynomial and trigonometric functions, which are computationally expensive in dealing with high dimensional problems due to their dependency on geometric complexity.
Wen Chen, Zhuojia Fu, Chih-Hao Chen
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Multiscale radial basis functions
, 2017Radial basis functions (RBFs) are a popular meshfree discretisation method for constructing high-order approximation spaces. They are used in various areas comprising, for example, scattered data approximation, computer graphics, machine learning, aeroelasticity and the geosciences.The approximation space is usually formed using the shifts of a fixed ...
H. Wendland
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Shape-adaptive radial basis functions
IEEE Transactions on Neural Networks, 1998Radial basis functions for discrimination and regression have been used with some success in a wide variety of applications. Here, we investigate the optimal choice for the form of the basis functions and present an iterative strategy for obtaining the function in a regression context using a conjugate gradient-based algorithm together with a ...
A R, Webb, S, Shannon
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STOCHASTIC RADIAL BASIS FUNCTIONS
International Journal of Neural Systems, 2001Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can
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