Springer, 2018. — 265 p. — (Lecture Notes in Computational Science and Engineering 123). — ISBN 3319754254.Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different flavors, are frequently the method of choice. This volume of LNCSE presents selected papers from the proceedings of the fourth workshop on sparse grids and applications, and demonstrates once again the importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with a range of applications including computational chemistry, computational fluid dynamics, and big data analytics, to name but a few. Contents Comparing Nested Sequences of Leja and PseudoGauss Points to Interpolate in 1D and Solve the Schroedinger Equation in 9D On the Convergence Rate of Sparse Grid Least Squares Regression Multilevel Adaptive Stochastic Collocation with Dimensionality Reduction Limiting Ranges of Function Values of Sparse Grid Surrogates Scalable Algorithmic Detection of Silent Data Corruption for High-Dimensional PDEs Sparse Grid Quadrature Rules Based on Conformal Mappings Solving Dynamic Portfolio Choice Models in Discrete Time Using Spatially Adaptive Sparse Grids Adaptive Sparse Grid Construction in a Context of Local Anisotropy and Multiple Hierarchical Parents Smolyak’s Algorithm: A Powerful Black Box for the Acceleration of Scientific Computations Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization
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