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Uncertainty Quantification and Predictive Computational Science

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Cover of 'Uncertainty Quantification and Predictive Computational Science'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Uncertainty Quantification and Predictive Science
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    Chapter 2 Probability and Statistics Preliminaries
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    Chapter 3 Input Parameter Distributions
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    Chapter 4 Local Sensitivity Analysis Based on Derivative Approximations
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    Chapter 5 Regression Approximations to Estimate Sensitivities
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    Chapter 6 Adjoint-Based Local Sensitivity Analysis
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    Chapter 7 Sampling-Based Uncertainty Quantification: Monte Carlo and Beyond
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    Chapter 8 Reliability Methods for Estimating the Probability of Failure
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    Chapter 9 Stochastic Projection and Collocation
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    Chapter 10 Gaussian Process Emulators and Surrogate Models
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    Chapter 11 Predictive Models Informed by Simulation, Measurement, and Surrogates
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    Chapter 12 Epistemic Uncertainties: Dealing with a Lack of Knowledge
Attention for Chapter 12: Epistemic Uncertainties: Dealing with a Lack of Knowledge
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