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By introducing a new concept of layer to sparse grid points, the sparse grid construction can become much efficient.
The introduction of separable functions also solves the issue of the non-rectangular sparse grid.
They are designed to exploit low effective dimensions and include sparse grid methods as special case.
Moreover, both of the sparse grid construction and interpolation algorithms can be done in parallel.
To cope with high dimensionalities we employ a hierarchical discretization scheme, the sparse grid combination technique.
The mean and standard deviation of the drag coefficient are computed using sparse grid techniques.
A sparse grid causes interpolation problems and dense grids increase the calibration effort.
The shapes should be approximated and examined before building sparse grid surrogates.
This leads to a set of uncoupled deterministic problems at the sparse grid collocation points.
On the sparse grid, the finite differences are used to approximate the derivatives.
Within each subdomain, a sparse grid interpolant is constructed using the classical Smolyak construction [S.
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