SIAM Journal on Scientific Computing
Computing expensive multivariate functions of fuzzy numbers using sparse grids
Fuzzy Sets and Systems
Sparse grid collocation schemes for stochastic natural convection problems
Journal of Computational Physics
Finite Elements in Analysis and Design
Variational capacitance modeling using orthogonal polynomial method
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Journal of Computational Physics
A domain adaptive stochastic collocation approach for analysis of MEMS under uncertainties
Journal of Computational Physics
ACC'09 Proceedings of the 2009 conference on American Control Conference
Computing expensive multivariate functions of fuzzy numbers using sparse grids
Fuzzy Sets and Systems
Journal of Computational Physics
Finite Elements in Analysis and Design
Variational capacitance extraction and modeling based on orthogonal polynomial method
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A generative approach for image-based modeling of tumor growth
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Low-Rank Tensor Krylov Subspace Methods for Parametrized Linear Systems
SIAM Journal on Matrix Analysis and Applications
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To recover or approximate smooth multivariate functions, sparse grids are superior to full grids due to a significant reduction of the required support nodes. The order of the convergence rate in the maximum norm is preserved up to a logarithmic factor. We describe three possible piecewise multilinear hierarchical interpolation schemes in detail and conduct a numerical comparison. Furthermore, we document the features of our sparse grid interpolation software package spinterp for MATLAB.