Sparse grids have gained increasing interest in recent years for
the numerical treatment of high-dimensional problems. Whereas
classical numerical discretization schemes fail in more than three
or four dimensions, sparse grids make it possible to overcome the
curse of dimensionality to some degree, extending the number of
dimensions that can be dealt with. This volume of LNCSE collects
the papers from the proceedings of the second workshop on sparse
grids and applications, demonstrating once again the importance of
this numerical discretization scheme. The selected articles present
recent advances on the numerical analysis of sparse grids as well
as efficient data structures, and the range of applications extends
to uncertainty quantification settings and clustering, to name but
a few examples.
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