Abstract
High-dimensional problems have gained interest in many disciplines such as Machine Learning, Data Analytics, and Uncertainty Quantification. These problems often require an adaptation of a model to the problem as standard methods do not provide an efficient description. Spatial adaptivity is one of these approaches that we investigate in this work. We introduce the Spatially Adaptive Combination Technique using a Split-Extend scheme—a spatially adaptive variant of the Sparse Grid Combination Technique—that recursively refines block adaptive full grids to get an efficient representation of local phenomena in functions. We discuss the method in the context of numerical quadrature and demonstrate that it is suited to refine efficiently for various test functions where common approaches fail. Trapezoidal quadrature rules as well as Gauss-Legendre quadrature are investigated to show its applicability to a wide range of quadrature formulas. Error estimates are used to automate the adaptation process which results in a parameter-free version of our refinement strategy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In case we consider boundary points we usually add these points to the level l 0 = 0 which results in \(I_{\boldsymbol {l}} = \{\boldsymbol {i} \in \mathbb {N}_0^d | i_j \in [0,2^{l_j}], i_j ~ \text{odd} \lor l_j = 0, j \in [d]\}\).
- 2.
If there is no unique largest entry, we can choose one of them arbitrarily.
- 3.
In fact our coarsening algorithm defines a surjective mapping of level vectors from the index set with l global to the one with l i if we exclude the level vectors that map to invalid level vectors.
- 4.
- 5.
References
H.-J. Bungartz and M. Griebel. Sparse grids. Acta Numerica, 13:147–269, 2004.
A. Genz. A package for testing multiple integration subroutines. Jan 1987.
T. Gerstner and M. Griebel. Numerical integration using sparse grids. Numerical Algorithms, 18(3):209, Jan 1998.
T. Gerstner and M. Griebel. Dimension–adaptive tensor–product quadrature. Computing, 71(1):65–87, Aug 2003.
M. Griebel, M. Schneider, and C. Zenger. A combination technique for the solution of sparse grid problems. In Iterative Methods in Lin. Alg., pages 263–281, 1992.
J. Noordmans and P. W. Hemker. Application of an adaptive sparse-grid technique to a model singular perturbation problem. Computing, 65(4):357–378, Dec 2000.
B. Peherstorfer, D. Pflüger, and H.-J. Bungartz. Density Estimation with Adaptive Sparse Grids for Large Data Sets, pages 443–451.
D. Pflüger. Spatially Adaptive Sparse Grids for High-Dimensional Problems. Verlag Dr. Hut, Munich, Aug 2010.
N. Wiener. The homogeneous chaos. American Journal of Mathematics, 1938.
C. Zenger. Sparse grids. In W. Hackbusch, editor, Parallel Algorithms for Partial Differential Equations, volume 31 of Notes on Numerical Fluid Mechanics, pages 241–251. Vieweg, 1991.
Acknowledgements
We want to thank Ionut-Gabriel Farcas, Michael Griebel, Friedrich Menhorn, Dirk Pflüger, Theresa Pollinger, Johannes Rentrop, Kilian Röhner and Paul-Christian Sarbu for many helpful discussions concerning Sparse Grids, quadrature and spatially adaptive schemes. This work was supported by the German Research Foundation (DFG) through the Priority Program Software for Exascale Computing (SPPEXA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Obersteiner, M., Bungartz, HJ. (2021). A Spatially Adaptive Sparse Grid Combination Technique for Numerical Quadrature. In: Bungartz, HJ., Garcke, J., Pflüger, D. (eds) Sparse Grids and Applications - Munich 2018. Lecture Notes in Computational Science and Engineering, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-81362-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-81362-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81361-1
Online ISBN: 978-3-030-81362-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)