Coverart for item
The Resource Sparse grids and applications : Miami 2016, Jochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors

Sparse grids and applications : Miami 2016, Jochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors

Label
Sparse grids and applications : Miami 2016
Title
Sparse grids and applications
Title remainder
Miami 2016
Statement of responsibility
Jochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors
Creator
Contributor
Editor
Subject
Genre
Language
eng
Summary
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.--
Member of
Assigning source
Provided by publisher
Cataloging source
N$T
Dewey number
004.36
Index
no index present
LC call number
QA188
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2016
http://bibfra.me/vocab/lite/meetingName
Workshop on Sparse Grids and Applications
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorDate
  • 1978-
  • 1984-
http://library.link/vocab/relatedWorkOrContributorName
  • Garcke, Jochen
  • Pflüger, Dirk
  • Webster, Clayton G.
  • Zhang, Guannan
Series statement
Lecture notes in computational science and engineering
Series volume
123
http://library.link/vocab/subjectName
  • Sparse matrices
  • Numerical analysis
  • Numerical grid generation (Numerical analysis)
Label
Sparse grids and applications : Miami 2016, Jochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors
Instantiates
Publication
Copyright
Note
Selected papers from the conference
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; 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
  • 4.1 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
  • 4 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
  • 4.1 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
  • 5.2 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
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319754260
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-75426-0
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319754260
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1041707031
  • (OCoLC)1041707031
Label
Sparse grids and applications : Miami 2016, Jochen Garcke, Dirk Pflüger, Clayton G. Webster, Guannan Zhang, editors
Publication
Copyright
Note
Selected papers from the conference
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; 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
  • 4.1 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
  • 4 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
  • 4.1 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
  • 5.2 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
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319754260
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-75426-0
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319754260
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1041707031
  • (OCoLC)1041707031

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