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000754252 020__ $$a9783319282626$$q(electronic book)
000754252 020__ $$a331928262X$$q(electronic book)
000754252 020__ $$z9783319282602
000754252 0247_ $$a10.1007/978-3-319-28262-6$$2doi
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000754252 24500 $$aSparse grids and applications$$h[electronic resource] :$$bStuttgart 2014 /$$cJochen Garcke, Kirk Pflüger, editor.
000754252 264_1 $$aCham, Switzerland :$$bSpringer,$$c2016.
000754252 300__ $$a1 online resource :$$billustrations
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000754252 337__ $$acomputer$$bc$$2rdamedia
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000754252 4901_ $$aLecture notes in computational science and engineering ;$$v109
000754252 504__ $$aIncludes bibliographical references.
000754252 5050_ $$aPeng Chen and Christoph Schwab: Adaptive Sparse Grid Model Order Reduction for Fast Bayesian Estimation and Inversion -- Fabian Franzelin and Dirk Pflüger: From Data to Uncertainty: An E_cient Integrated Data-Driven Sparse Grid Approach to Propagate Uncertainty -- Helmut Harbrecht and Michael Peters: Combination Technique Based Second Moment Analysis for Elliptic PDEs on Random Domains -- Brendan Harding: Adaptive sparse grids and extrapolation techniques -- Philipp Hupp and Riko Jacob: A Cache-Optimal Alternative to the Unidirectional Hierarchization Algorithm -- Valeriy Khakhutskyy and Markus Hegland: Spatially-Dimension- Adaptive Sparse Grids for Online Learning -- Katharina Kormann and Eric Sonnendrücker: Sparse Grids for the Vlasov-Poisson Equation -- Fabio Nobile, Lorenzo Tamellini, Francesco Tesei and Raul Tempone: An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient -- David Pfander, Alexander Heinecke, and Dirk Pflüger: A New Subspace-Based Algorithm for E_cient Spatially Adaptive Sparse Grid Regression, Classification and Multi- Evaluation -- Sharif Rahman, Xuchun Ren, and Vaibhav Yadav: High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition -- Jie Shen, Yingwei Wang, and Haijun Yu: E_cient Spectral-Element Methods for the Electronic Schrödinger Equation -- Hoang Tran, Clayton G. Webster, and Guannan Zhang: A Sparse Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models -- Julian Valentin and Dirk Pflüger: Hierarchical Gradient-Based Optimization with BSplines on Sparse Grids.
000754252 506__ $$aAccess limited to authorized users.
000754252 520__ $$aThis volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.
000754252 650_0 $$aSparse matrices$$vCongresses.
000754252 650_0 $$aNumerical analysis$$vCongresses.
000754252 7001_ $$aGarcke, Jochen,$$eeditor.
000754252 7001_ $$aPflüger, Dirk,$$eeditor.
000754252 77608 $$iPrint version:$$z9783319282602
000754252 830_0 $$aLecture notes in computational science and engineering ;$$v109.
000754252 85280 $$bebk$$hSpringerLink
000754252 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-28262-6$$zOnline Access$$91397441.1
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