The technique of sparse grids allows to overcome the curse of dimensionality, which prevents the use of classical numerical discretization schemes in more than three or four dimensions, under suitable regularity assumptions. The approach is obtained from a multi-scale basis by a tensor product construction and subsequent truncation of the resulting multiresolution series expansion. This entry level article gives an introduction to sparse grids and the sparse grid combination technique
Generative dimensionality reduction methods play an important role in machine learning applications ...
In this article we introduce approximation spaces for parabolic problems which are based on the tens...
Sparse grids have gained increasing interest in recent years for the numerical treatment of high-dim...
Abstract The technique of sparse grids allows to overcome the curse of dimension-ality, which preven...
In the recent decade, there has been growing interest in the numerical treatment of high-dimensional...
For the approximation of multidimensional functions, using classical numerical discretization scheme...
When working on multidimensional problems, the number of points needed when using a tensor product g...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
Sparse grids are a recently introduced new technique for discretizing partial differential equations...
Our goal is to present an elementary approach to the analysis and programming of sparse grid finite ...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where clas...
getragenen Sonderforschungsbereiches 611 an der Universität Bonn ent-standen und als Manuskript verv...
Sparse grids have become an important tool to reduce the number of degrees of freedom of discretizat...
In real-world applications mathematical models often involve more than one variable. For example, a...
Generative dimensionality reduction methods play an important role in machine learning applications ...
In this article we introduce approximation spaces for parabolic problems which are based on the tens...
Sparse grids have gained increasing interest in recent years for the numerical treatment of high-dim...
Abstract The technique of sparse grids allows to overcome the curse of dimension-ality, which preven...
In the recent decade, there has been growing interest in the numerical treatment of high-dimensional...
For the approximation of multidimensional functions, using classical numerical discretization scheme...
When working on multidimensional problems, the number of points needed when using a tensor product g...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
Sparse grids are a recently introduced new technique for discretizing partial differential equations...
Our goal is to present an elementary approach to the analysis and programming of sparse grid finite ...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where clas...
getragenen Sonderforschungsbereiches 611 an der Universität Bonn ent-standen und als Manuskript verv...
Sparse grids have become an important tool to reduce the number of degrees of freedom of discretizat...
In real-world applications mathematical models often involve more than one variable. For example, a...
Generative dimensionality reduction methods play an important role in machine learning applications ...
In this article we introduce approximation spaces for parabolic problems which are based on the tens...
Sparse grids have gained increasing interest in recent years for the numerical treatment of high-dim...