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000856155 019__ $$a1042079994$$a1047689947
000856155 020__ $$a9783319754260$$q(electronic book)
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000856155 020__ $$z9783319754253
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000856155 0247_ $$a10.1007/978-3-319-75426-0$$2doi
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000856155 1112_ $$aWorkshop on Sparse Grids and Applications$$n(4th :$$d2016 :$$cMiami, Fla.)
000856155 24510 $$aSparse grids and applications :$$bMiami 2016 /$$cJochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors.
000856155 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018]
000856155 264_4 $$c©2018
000856155 300__ $$a1 online resource.
000856155 336__ $$atext$$btxt$$2rdacontent
000856155 337__ $$acomputer$$bc$$2rdamedia
000856155 338__ $$aonline resource$$bcr$$2rdacarrier
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000856155 4901_ $$aLecture notes in computational science and engineering ;$$v123
000856155 500__ $$aSelected papers from the conference.
000856155 504__ $$aIncludes bibliographical references.
000856155 5050_ $$aIntro; Preface; Contents; Contributors; Comparing Nested Sequences of Leja and PseudoGauss Points to Interpolate in 1D and Solve the Schroedinger Equation in 9D; 1 Introduction; 2 Interpolation; 3 The Importance of Nesting; 3.1 PseudoGauss Nested Points; 3.2 Leja Nested Points; 4 Lebesgue Constants; 5 Comparison Between Leja Points and PseudoGauss Points in Collocation Calculations; 6 Conclusion; References; On the Convergence Rate of Sparse Grid Least Squares Regression; 1 Introduction; 2 Least-Squares Regression; 3 Full Grids and Sparse Grids; 4 Error Analysis.
000856155 5058_ $$a4.1 Well-Posedness and Error Decay4.2 Application to Sparse Grids; 5 Numerical Experiments; 5.1 Error Decay; 5.2 Balancing the Error; 6 Conclusion; References; Multilevel Adaptive Stochastic Collocation with Dimensionality Reduction; 1 Introduction; 2 Adaptivity with Sparse Grids; 2.1 Interpolation on Spatially-Adaptive Sparse Grids; 2.2 Interpolation with Dimension-Adaptive Sparse Grids; 3 Multilevel Stochastic Collocation with Dimensionality Reduction; 3.1 Generalized Polynomial Chaos; 3.2 Multilevel Approaches for Generalized Polynomial Chaos; 3.3 Stochastic Dimensionality Reduction.
000856155 5058_ $$a4 Numerical Results4.1 Second-Order Linear Oscillator with External Forcing; 4.2 A simple Fluid-Structure Interaction Example; 5 Conclusions and Outlook; References; Limiting Ranges of Function Values of Sparse Grid Surrogates; 1 Introduction; 2 Sparse Grids; 2.1 Hierarchical Ancestors and the Fundamental Property; 2.2 Interpolation on Sparse Grids; 3 Limiting Ranges of Sparse Grid Function Values; 3.1 Limitation from Above and Below; 3.2 Minimal Extension Set; 3.3 Computing Coefficients of the Extension Set; 3.4 Intersection Search; 4 Approximation of Gaussians with Extended Sparse Grids.
000856155 5058_ $$a4.1 Intersection Search and Candidate Sets for Regular Sparse Grids4.2 Extension Sets and Convergence for Regular Grids; 4.3 Extension Sets for Adaptively Refined Grids; 5 Conclusions; References; Scalable Algorithmic Detection of Silent Data Corruption for High-Dimensional PDEs; 1 Introduction; 1.1 High-Dimensional PDEs in High-Performance Computing; 2 Theory of the Classical Combination Technique; 3 The Combination Technique in Parallel; 4 Dealing with System Faults; 5 Detecting and Recovering from SDC; 5.1 Method 1: Comparing Combination Solutions Pairwise via a Maximum Norm.
000856155 5058_ $$a5.2 Method 2: Comparing Combination Solutions via their Function Values Directly5.3 Cost and Parallelization; 5.4 Detection Rates; 6 Numerical Tests; 6.1 Experimental Setup; 6.2 SDC Injection; 6.3 Results: Detection Rates and Errors; 6.4 Results: Scaling; 6.5 Dealing with False Positives; 7 Extensions to Quantities of Interest; 8 Conclusion; References; Sparse Grid Quadrature Rules Based on Conformal Mappings; 1 Introduction and Background; 2 Transformed Quadrature Rules; 2.1 Standard One-Dimensional Quadrature Rules; 2.2 Sparse Quadrature for High Dimensional Integrals.
000856155 506__ $$aAccess limited to authorized users.
000856155 520__ $$aSparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different flavors, are frequently the method of choice. This volume of LNCSE presents selected papers from the proceedings of the fourth workshop on sparse grids and applications, and demonstrates once again the importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with a range of applications including computational chemistry, computational fluid dynamics, and big data analytics, to name but a few.--$$cProvided by publisher.
000856155 588__ $$aOnline resource; title from PDF title page (viewed June 25, 2018).
000856155 650_0 $$aSparse matrices$$vCongresses.
000856155 650_0 $$aNumerical analysis$$vCongresses.
000856155 650_0 $$aNumerical grid generation (Numerical analysis)$$vCongresses.
000856155 7001_ $$aGarcke, Jochen,$$eeditor.
000856155 7001_ $$aPflüger, Dirk,$$eeditor.
000856155 7001_ $$aWebster, Clayton G.$$q(Clayton Garrett),$$d1978-$$eeditor.
000856155 7001_ $$aZhang, Guannan,$$d1984-$$eeditor.
000856155 77608 $$iPrint version:$$aWorkshop on Sparse Grids and Applications (4th : 2016 : Miami, Fla.).$$tSparse grids and applications.$$dCham, Switzerland : Springer, [2018]$$z3319754254$$z9783319754253$$w(OCoLC)1019639549
000856155 830_0 $$aLecture notes in computational science and engineering ;$$v123.
000856155 852__ $$bebk
000856155 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-75426-0$$zOnline Access$$91397441.1
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