Sparse Grids and Applications - Stuttgart 2014: Lecture Notes in Computational Science and Engineering, cartea 109
Editat de Jochen Garcke, Dirk Pflügeren Limba Engleză Hardback – 18 mar 2016
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Specificații
ISBN-13: 9783319282602
ISBN-10: 3319282603
Pagini: 352
Ilustrații: VIII, 336 p. 85 illus., 24 illus. in color.
Dimensiuni: 160 x 241 x 25 mm
Greutate: 0.69 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computational Science and Engineering
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Cham, Switzerland
ISBN-10: 3319282603
Pagini: 352
Ilustrații: VIII, 336 p. 85 illus., 24 illus. in color.
Dimensiuni: 160 x 241 x 25 mm
Greutate: 0.69 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computational Science and Engineering
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Peng Chen and Christoph Schwab: Adaptive Sparse Grid Model Order Reduction for Fast Bayesian Estimation and Inversion.- Fabian Franzelin and Dirk Pflüger: From Data to Uncertainty: An E_cient Integrated Data-Driven Sparse Grid Approach to Propagate Uncertainty.- Helmut Harbrecht and Michael Peters: Combination Technique Based Second Moment Analysis for Elliptic PDEs on Random Domains.- Brendan Harding: Adaptive sparse grids and extrapolation techniques.- Philipp Hupp and Riko Jacob: A Cache-Optimal Alternative to the Unidirectional Hierarchization Algorithm.- Valeriy Khakhutskyy and Markus Hegland: Spatially-Dimension- Adaptive Sparse Grids for Online Learning.- Katharina Kormann and Eric Sonnendrücker: Sparse Grids for the Vlasov–Poisson Equation.- Fabio Nobile, Lorenzo Tamellini, Francesco Tesei and Raul Tempone: An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient.- David Pfander, Alexander Heinecke, and Dirk Pflüger: A New Subspace-Based Algorithm for E_cient Spatially Adaptive Sparse Grid Regression, Classification and Multi- Evaluation.- Sharif Rahman, Xuchun Ren, and Vaibhav Yadav: High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition.- Jie Shen, Yingwei Wang, and Haijun Yu: E_cient Spectral-Element Methods for the Electronic Schrödinger Equation.- Hoang Tran, Clayton G. Webster, and Guannan Zhang: A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models.- Julian Valentin and Dirk Pflüger: Hierarchical Gradient-Based Optimization with BSplines on Sparse Grids.