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Uncertainty Quantification in Scientific Computing

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Cover of 'Uncertainty Quantification in Scientific Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Uncertainties Using Genomic Information for Evidence-Based Decisions
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    Chapter 2 Considerations of Uncertainty in Regulatory Decision Making
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    Chapter 3 An Industrial Viewpoint on Uncertainty Quantification in Simulation: Stakes, Methods, Tools, Examples
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    Chapter 4 Living with Uncertainty
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    Chapter 5 Uncertainty and Sensitivity Analysis: From Regulatory Requirements to Conceptual Structure and Computational Implementation
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    Chapter 6 Bayes Linear Analysis for Complex Physical Systems Modeled by Computer Simulators
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    Chapter 7 Verified Computation with Probabilities
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    Chapter 8 Defects, Scientific Computation and the Scientific Method
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    Chapter 9 Parametric and Uncertainty Computations with Tensor Product Representations
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    Chapter 10 Uncertainty Quantification in Scientific Computing
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    Chapter 11 Measuring Uncertainty in Scientific Computation Using Numerica 21’s Test Harness
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    Chapter 12 Numerical Aspects in the Evaluation of Measurement Uncertainty
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    Chapter 13 Model-Based Interpolation, Prediction, and Approximation
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    Chapter 14 Uncertainty Quantification for Turbulent Mixing Flows: Rayleigh-Taylor Instability
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    Chapter 15 From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches
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    Chapter 16 Efficient Computation of Observation Impact in 4D-Var Data Assimilation
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    Chapter 17 Interval Based Finite Elements for Uncertainty Quantification in Engineering Mechanics
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    Chapter 18 Reducing the Uncertainty When Approximating the Solution of ODEs
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    Chapter 19 Uncertainties in Predictions of Material Performance Using Experimental Data That Is Only Distantly Related to the System of Interest
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    Chapter 20 A Note on Uncertainty in Real-Time Analytics
Attention for Chapter 15: From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches
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Chapter title
From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches
Chapter number 15
Book title
Uncertainty Quantification in Scientific Computing
Published by
Springer, Berlin, Heidelberg, January 2012
DOI 10.1007/978-3-642-32677-6_15
Book ISBNs
978-3-64-232676-9, 978-3-64-232677-6
Authors

Kristin Potter, Paul Rosen, Chris R. Johnson

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Germany 3 2%
Switzerland 1 <1%
Colombia 1 <1%
Austria 1 <1%
Australia 1 <1%
United Kingdom 1 <1%
France 1 <1%
China 1 <1%
Other 3 2%
Unknown 155 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 33%
Student > Master 23 13%
Researcher 17 10%
Student > Bachelor 14 8%
Student > Doctoral Student 9 5%
Other 26 15%
Unknown 27 16%
Readers by discipline Count As %
Computer Science 79 46%
Engineering 16 9%
Agricultural and Biological Sciences 7 4%
Psychology 7 4%
Earth and Planetary Sciences 5 3%
Other 27 16%
Unknown 32 18%