We give a new fast method for evaluating sprectral approximations of non-linear polynomial functionals. We prove that the new algorithm is convergent if the functions considered are smooth enough, under a general assumption on the spectral eigenfunctions that turns out to be satisfied in many cases, including the Fourier and Hermite basis
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
A straightforward discretisation of high-dimensional problems often leads to a curse of dimensions a...
Common methods for the calculation of the spectral factorization rely on an approximation of the giv...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
Multivariate global polynomial approximations – such as polynomial chaos or stochastic collocation m...
Abstract. Polynomial approximations of computationally intensive models are central to uncertainty q...
AbstractWe develop a fast discrete algorithm for computing the sparse Fourier expansion of a functio...
Sparse polynomial approximation has become indispensable for approximating smooth, high- or infinite...
In this paper, we extent the classical spectral approximation theory for compact and bounded operato...
This book is a basic and comprehensive introduction to the use of spectral methods for the approxima...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
This book is a basic and comprehensive introduction to the use of spectral methods for the approxima...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
A straightforward discretisation of high-dimensional problems often leads to a curse of dimensions a...
Common methods for the calculation of the spectral factorization rely on an approximation of the giv...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functional...
Multivariate global polynomial approximations – such as polynomial chaos or stochastic collocation m...
Abstract. Polynomial approximations of computationally intensive models are central to uncertainty q...
AbstractWe develop a fast discrete algorithm for computing the sparse Fourier expansion of a functio...
Sparse polynomial approximation has become indispensable for approximating smooth, high- or infinite...
In this paper, we extent the classical spectral approximation theory for compact and bounded operato...
This book is a basic and comprehensive introduction to the use of spectral methods for the approxima...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
This book is a basic and comprehensive introduction to the use of spectral methods for the approxima...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
Spectral methods represent a family of methods for the numerical approximation of partial differenti...
A straightforward discretisation of high-dimensional problems often leads to a curse of dimensions a...
Common methods for the calculation of the spectral factorization rely on an approximation of the giv...