1  20
Next
 Chichester ; Hoboken, NJ : Wiley, c2004.
 Description
 Book — xi, 219 p. : ill. ; 24 cm.
 Summary

 PREFACE
 .1. A WORKED EXAMPLE.1.1 A simple model.1.2 Modulus version of the simple model.1.3 Sixfactor version of the simple model.1.4 The simple model 'by groups'.1.5 The (less) simple correlatedinput model.1.6 Conclusions
 .2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT.2.1 Examples at a glance.2.2 What is sensitivity analysis?2.3 Properties of an ideal sensitivity analysis method.2.4 Defensible settings for sensitivity analysis.2.5 Caveats
 .3. TEST CASES.3.1 The jumping man. Applying variancebased methods.3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variancebased methods.3.3 A model of fish population dynamics. Applying the method of Morris.3.4 The Level E model. Radionuclide migration in the geosphere. Applying variancebased methods and Monte Carlo filtering.3.5 Two spheres. Applying variance based methods in estimation/calibration problems.3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems.3.7 An analytical example. Applying the method of Morris
 .4. THE SCREENING EXERCISE.4.1 Introduction.4.2 The method of Morris.4.3 Implementing the method.4.4 Putting the method to work: an analytical example.4.5 Putting the method to work: sensitivity analysis of a fish population model.4.6 Conclusions
 .5. METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT.5.1 The settings.5.2 Factors Prioritisation Setting.5.3 Firstorder effects and interactions.5.4 Application of Si to Setting 'Factors Prioritisation'.5.5 More on variance decompositions.5.6 Factors Fixing (FF) Setting.5.7 Variance Cutting (VC) Setting.5.8 Properties of the variance based methods.5.9 How to compute the sensitivity indices: the case of orthogonal input.5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST).5.10 How to compute the sensitivity indices: the case of nonorthogonal input.5.11 Putting the method to work: the Level E model.5.11.1 Case of orthogonal input factors.5.11.2 Case of correlated input factors.5.12 Putting the method to work: the bungee jumping model.5.13 Caveats
 .6. SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS.6.1 Model calibration and Factors Mapping Setting.6.2 Monte Carlo filtering and regionalised sensitivity analysis.6.2.1 Caveats.6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio.6.4 Putting MC filtering and RSA to work: the Level E test case.6.5 Bayesian uncertainty estimation and global sensitivity analysis.6.5.1 Bayesian uncertainty estimation.6.5.2 The GLUE case.6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation.6.5.4 Implementation of the method.6.6 Putting Bayesian analysis and global SA to work: two spheres.6.7 Putting Bayesian analysis and global SA to work: a chemical experiment.6.7.1 Bayesian uncertainty analysis (GLUE case).6.7.2 Global sensitivity analysis.6.7.3 Correlation analysis.6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model.6.8 Caveats
 .7. HOW TO USE SIMLAB.7.1 Introduction.7.2 How to obtain and install SIMLAB.7.3 SIMLAB main panel.7.4 Sample generation.7.4.1 FAST.7.4.2 Fixed sampling.7.4.3 Latin hypercube sampling (LHS).7.4.4 The method of Morris.7.4.5 QuasiRandom LpTau.7.4.6 Random.7.4.7 Replicated Latin Hypercube (rLHS).7.4.8 The method of Sobol'.7.4.9 How to induce dependencies in the input factors.7.5 How to execute models.7.6 Sensitivity analysis
 .8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE.REFERENCES.INDEX.
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QA402.3 .S453 2004  Unknown 
Online 3. Local and Global Sensitivity Analysis of a Reactive Transport Model Simulating Floodplain Redox Cycling [2021]
 Perzan, Zach (Author)
 November 9, 2022; November 9, 2021
 Description
 Book
 Summary

Reactive transport models (RTMs) are essential tools that simulate the coupling of advective, diffusive, and reactive processes in the subsurface, but their complexity makes them difficult to understand, develop and improve without accompanying statistical analyses. Although global sensitivity analysis (SA) can address these issues, the computational cost associated with most global SA techniques limits their use with RTMs. In this study, we apply distancebased generalized sensitivity analysis (DGSA), a novel and computationally efficient method of global SA, to a floodplainscale RTM and compare DGSA results to those from local SA. Our test case focuses on the impact of 17 uncertain environmental parameters on spatially and temporally variable redox conditions within a floodplain aquifer. The input parameters considered include flow and diffusion rates, geochemical reaction rates, and the spatial distribution of sediment facies. Sensitivity was evaluated for three distinct components of the model response, encompassing both multidimensional and categorical output. Parameter rankings differ between local SA and DGSA, due to nonlinear effects of individual parameters and interaction effects between parameters. DGSA results show that fluid residence time, which is controlled by aquifer permeability, generally exerts a stronger control on redox conditions than do geochemical reaction rates. Sensitivity indices also demonstrate that sulfate reduction is key for establishing and maintaining reducing conditions throughout the aquifer. These results provide insights into the key drivers of heterogeneous redox processes within floodplain aquifers, as well as the main sources of uncertainty when modeling complex subsurface systems.
 Digital collection
 Stanford University Open Access Articles
4. A method for reducing the sensitivity of optimal nonlinear systems to parameter uncertainty [1971]
 Elliott, Jarrell R.
 Washington, D.C. : National Aeronautics and Space Administration ; Springfield, VA : For sale by the Clearinghouse for Federal Scientific and Technical Information, 1971.
 Description
 Book — 40 p. : ill. ; 27 cm.
SAL3 (offcampus storage)
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NASA TN D6218  Unknown 
 Cacuci, Dan Gabriel, author.
 Cham, Switzerland : Springer, [2023]
 Description
 Book — 1 online resource (xvii, 463 pages) : illustrations
 Summary

 Chapter1. 1stOrder Sensitivity Analysis of the OECD/NEA PERP Reactor Physics Benchmark
 Chapter2. 2ndOrder Sensitivities of the PERP Benchmark to the Microscopic Total and Capture Cross Sections
 Chapter3. 2ndOrder Sensitivities of the PERP Benchmark to the Microscopic Scattering Cross Sections
 Chapter4. 2ndOrder Sensitivities of the PERP Benchmark to the Microscopic Fission Cross Sections
 Chapter5. 2ndOrder Sensitivities of the PERP Benchmark to the Average Number of Neutrons per Fission
 Chapter6. 2ndOrder Sensitivities of the PERP Benchmark to the Spontaneous Fission Source Parameters
 Chapter7. 2ndOrder Sensitivities of the PERP Benchmark to the Isotopic Number Densities
 Chapter8. 3rdOrder Sensitivities of the PERP Benchmark
 Chapter9. 4thOrder Sensitivities of the PERP Benchmark
 Chapter10. Overall Impact of 1st, 2nd, 3rd, and 4thOrder Sensitivities on the PERP Benchmark's Response Uncertainties.
 Cacuci, Dan Gabriel, author.
 Cham, Switzerland : Springer, [2023]
 Description
 Book — 1 online resource (xii, 369 pages) : illustrations
 Summary

 Part A: FunctionValued Responses. Chapter 1: The First and SecondOrder Comprehensive Adjoint Sensitivity Analysis Methodologies for Nonlinear Systems with FunctionValued Responses
 Chapter 2: The ThirdOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM3) for Nonlinear Systems with FunctionValued Responses
 Chapter 3: The FourthOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM4) for Nonlinear Systems with FunctionValued Responses
 Chapter 4: The NthOrder Adjoint Sensitivity Analysis Methodology (CASAMN) for Nonlinear Systems with FunctionValued Responses
 Part B: ScalarValued Responses
 Part B: ScalarValued Responses
 Chapter 5: The FourthOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM4) for Nonlinear Systems with ScalarValued Responses
 Chapter 6: The NthOrder Adjoint Sensitivity Analysis Methodology (CASAMN) for Nonlinear Systems with ScalarValued Responses
 Chapter 7: Applications of CASAM to Uncertainty Analysis.
 Chichester, England ; Hoboken, NJ : John Wiley, c2008.
 Description
 Book — x, 292 p. : ill. ; 24 cm.
 Summary

 Preface.
 1. Introduction to Sensitivity Analysi. 1.1 Models and Sensitivity Analysis. 1.1.1 Definition. 1.1.2 Models. 1.1.3 Models and Uncertainty. 1.1.4 How to Set Up Uncertainty and Sensitivity Analyses. 1.1.5 Implications for Model Quality. 1.2 Methods and Settings for Sensitivity Analysis  An Introduction. 1.2.1 Local versus Global. 1.2.2 A Test Model. 1.2.3 Scatterplots versus Derivatives. 1.2.4 Sigmanormalized Derivatives. 1.2.5 Monte Carlo and Linear Regression. 1.2.6 Conditional Variances  First Path. 1.2.7 Conditional Variances  Second Path. 1.2.8 Application to Model (1.3). 1.2.9 A First Setting: 'Factor Prioritization' 1.2.10 Nonadditive Models. 1.2.11 Higherorder Sensitivity Indices. 1.2.12 Total Effects. 1.2.13 A Second Setting: 'Factor Fixing'. 1.2.14 Rationale for Sensitivity Analysis. 1.2.15 Treating Sets. 1.2.16 Further Methods. 1.2.17 Elementary Effect Test. 1.2.18 Monte Carlo Filtering. 1.3 Nonindependent Input Factors. 1.4 Possible Pitfalls for a Sensitivity Analysis. 1.5 Concluding Remarks. 1.6 Exercises. 1.7 Answers. 1.8 Additional Exercises. 1.9 Solutions to Additional Exercises.
 2. Experimental Designs. 2.1 Introduction. 2.2 Dependency on a Single Parameter. 2.3 Sensitivity Analysis of a Single Parameter. 2.3.1 Random Values. 2.3.2 Stratified Sampling. 2.3.3 Mean and Variance Estimates for Stratified Sampling. 2.4 Sensitivity Analysis of Multiple Parameters. 2.4.1 Linear Models. 2.4.2 Oneatatime (OAT) Sampling. 2.4.3 Limits on the Number of Influential Parameters. 2.4.4 Fractional Factorial Sampling. 2.4.5 Latin Hypercube Sampling. 2.4.6 Multivariate Stratified Sampling. 2.4.7 Quasirandom Sampling with Lowdiscrepancy Sequences. 2.5 Group Sampling. 2.6 Exercises. 2.7 Exercise Solutions.
 3. Elementary Effects Method. 3.1 Introduction. 3.2 The Elementary Effects Method. 3.3 The Sampling Strategy and its Optimization. 3.4 The Computation of the Sensitivity Measures. 3.5 Working with Groups. 3.6 The EE Method Step by Step. 3.7 Conclusions. 3.8 Exercises. 3.9 Solutions.
 4. Variancebased Methods. 4.1 Different Tests for Different Settings. 4.2 Why Variance? 4.3 Variancebased Methods. A Brief History. 4.4 Interaction Effects. 4.5 Total Effects. 4.6 How to Compute the Sensitivity Indices. 4.7 FAST and Random Balance Designs. 4.8 Putting the Method to Work: the Infection Dynamics Model. 4.9 Caveats. 4.10 Exercises.
 5. Factor Mapping and Metamodelling. 5.1 Introduction. 5.2 Monte Carlo Filtering (MCF). 5.2.1 Implementation of Monte Carlo Filtering. 5.2.2 Pros and Cons. 5.2.3 Exercises. 5.2.4 Solutions. 5.2.5 Examples. 5.3 Metamodelling and the HighDimensional Model Representation. 5.3.1 Estimating HDMRs and Metamodels. 5.3.2 A Simple Example. 5.3.3 Another Simple Example. 5.3.4 Exercises. 5.3.5 Solutions to Exercises. 5.4 Conclusions.
 6. Sensitivity Analysis: from Theory to Practice. 6.1 Example
 1: a Composite Indicator. 6.1.1 Setting the Problem. 6.1.2 A Composite Indicator Measuring Countries' Performance in Environmental Sustainability. 6.1.3 Selecting the Sensitivity Analysis Method. 6.1.4 The Sensitivity Analysis Experiment and its Results. 6.1.5 Conclusions. 6.2 Example
 2: Importance of Jumps in Pricing Options. 6.2.1 Setting the Problem. 6.2.2 The Heston Stochastic Volatility Model with Jumps. 6.2.3 Selecting a Suitable Sensitivity Analysis Method. 6.2.4 The Sensitivity Analysis Experiment. 6.2.5 Conclusions. 6.3 Example
 3: a Chemical Reactor. 6.3.1 Setting the Problem. 6.3.2 Thermal Runaway Analysis of a Batch Reactor. 6.3.3 Selecting the Sensitivity Analysis Method. 6.3.4 The Sensitivity Analysis Experiment and its Results. 6.3.5 Conclusions. 6.4 Example
 4: a Mixed UncertaintySensitivity Plot. 6.4.1 In Brief. 6.5 When to use What? Afterword. Bibliography. Index.
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 Borggaard, Jeff.
 Hampton, Va. : National Aeronautics and Space Administration, Langley Research Center ; [Springfield, Va. : National Technical Information Service, distributor, 1994]
 Description
 Book — 1 v.
 Online
Green Library
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NAS 1.26:191598  Unknown 
9. Sensitivity and uncertainty analysis [2003 ]
 Cacuci, Dan Gabriel
 Boca Raton : Chapman & Hall/CRC Press, c2003
 Description
 Book — v. : ill. ; 25 cm.
 Summary

 A Comparative Review of Sensitivity and Uncertainty Analysis Methods for LargeScale Systems. Applications of the Adjoint Sensitivity Analysis Procedure (ASAP) to TwoPhase Flow Systems. Forward and Adjoint Sensitivity Analysis Procedures for Augmented. Systems. Forward and Adjoint Sensitivity Analysis Procedures for Responses Defined At Critical Points. Using the ASAP to Gain New Insights Into Paradigm Atmospheric Sciences Problems. Adjoint Sensitivity Analysis Procedure for Operational Meteorological Applications. References. Index.
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 As computerassisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable investigative scientific tools in their own right. While most techniques used for these analyses are well documented, there has yet to appear a systematic treatment of the method based on adjoint operators, which is applicable to a much wider variety of problems than methods traditionally used in control theory. This book fills that gap, focusing on the mathematical underpinnings of the Adjoint Sensitivity Analysis Procedure (ASAP) and the use of deterministically obtained sensitivities for subsequent uncertainty analysis.
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There are many methods for performing sensitivity and uncertainty analysis. Two of the modern  and most useful  deterministic methods, the Adjoint Sensitivity Analysis Procedure (ASAP) and the Global Adjoint Sensitivity Analysis (GASAP), received detailed theoretical treatment in Volume I of this book. This volume extends the underlying theory of these methods into practice with a focus on their application to largescale systems. "Sensitivity and Uncertainty Analysis Volume II: Applications to LargeScale Systems" begins with a review of the most prominent screening design, statistical, and deterministic methods.The authors then explore applications of the ASAP to transient onedimensional twophase flow problems, in particular its implementation into a largescale code that simulates the thermalhydraulic characteristics of light water nuclear reactors. They go on to build the theoretical foundation for the modular implementation of the ASAP for complex simulations systems and present the general sensitivity theory for the response functional of a physical system defined at critical points.The remaining chapters are devoted to applications of the ASAP to sensitivity analyses of largescale models used for numerical weather prediction and climatic research and simulation. The examples presented clearly demonstrate the advantages of using the ASAP for largescale systems characterized by many variables and parameters, particularly its exceptional computational efficiency. Rigorous but accessible, this book will build a thorough familiarity with ASAP and its advantages and make ASAP a valuable addition to your analytical toolbox.
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QA402.3 .C255 2003 V.1  Available 
 Eslami, Mansour.
 Berlin ; New York : SpringerVerlag, c1994.
 Description
 Book — xvii, 600 p. : ill. ; 25 cm.
 Online
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QA402.3 .E85 1994  Available 
 Laporte, Emmanuel, 1971
 Boston : Birkhäuser, c2003.
 Description
 Book — xiii, 194 p. : ill. ; 25 cm. + 1 CDROM (4 3/4 in.).
 Summary

 Outline and Notation * Basic Formulations * Finite Dimensional Optimization * Newton's Algorithms * Constrained Optimization * Automatic Differentiation * Computing Gradients by Adjoint States * Applications * One Shot Methods * Conclusions * Appendix A: Subroutine cubspl * Appendix B: Prototype Programmes for the Optimization Code * Appendix C: Odyssee User's Manual (Short Version) * Appendix D: A Subroutine Computing the Gradient with Respect to the Grid for the Steady Aerodynamic Example * Bibliography * Index.
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QA402.3 .L37 2003  Unknown 
 Kleiber, Michał.
 Chichester [England] ; New York : John Wiley, 1997.
 Description
 Book — xv, 406 p. : ill. ; 25 cm.
 Summary

 PRELIMINARIES. Motivation: Sensitivity and LargeScale Systems. Nonlinear Solid Mechanics: Continuous and SemiDiscretized Formulation. Concepts of Sensitivity Analysis for Linear Systems.THE SENSITIVITY OF NONLINEAR SYSTEMS. The Basic Concepts of Nonlinear QuasiStatic Problems at Regular States. Inelastic Systems. Shape Sensitivity. Buckling and PostBuckling. Nonlinear Dynamics. Metal Forming Using the Flow Approach. Nonlinear Thermal Systems. Appendices. References. Index. Glossary of Symbols.
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TA350 .K654 1997  Available 
13. A method for reducing the sensitivity of optimal nonlinear systems to parameter uncertainty [1971]
 Elliott, Jarrell R., author.
 Washington, D.C. : National Aeronautics and Space Administration, June 1971.
 Description
 Book — 1 online resource (40 pages) : illustrations.
14. Missing data in longitudinal studies : strategies for Bayesian modeling and sensitivity analysis [2008]
 Daniels, M. J.
 Boca Raton : Chapman & Hall/CRC, c2008.
 Description
 Book — xx, 303 p. : ill. ; 25 cm.
 Summary

 PREFACE Description of Motivating Examples Overview DoseFinding Trial of an Experimental Treatment for Schizophrenia Clinical Trial of Recombinant Human Growth Hormone (rhGH) for Increasing Muscle Strength in the Elderly Clinical Trials of Exercise as an Aid to Smoking Cessation in Women: The Commit to Quit Studies Natural History of HIV Infection in Women: HIV Epidemiology Research Study (HERS) Cohort Clinical Trial of Smoking Cessation among Substance Abusers: OASIS Study Equivalence Trial of Competing Doses of AZT in HIVInfected Children: Protocol 128 of the AIDS Clinical Trials Group Regression Models Overview Preliminaries Generalized Linear Models Conditionally Specified Models Directly Specified (Marginal) Models Semiparametric Regression Interpreting Covariate Effects Further Reading Methods of Bayesian Inference Overview Likelihood and Posterior Distribution Prior Distributions Computation of the Posterior Distribution Model Comparisons and Assessing Model Fit Nonparametric Bayes Further Reading Bayesian Analysis using Data on Completers Overview Model Selection and Inference with a Multivariate Normal Model: Analysis of the Growth Hormone Clinical Study Inference with a Normal Random Effects Model: Analysis of the Schizophrenia Clinical Trial Model Selection and Inference for Binary Longitudinal Data: Analysis of CTQ I Summary Missing Data Mechanisms and Longitudinal Data Introduction Full vs. Observed Data FullData Models and Missing Data Mechanisms Assumptions about Missing Data Mechanism Missing at Random Applied to Dropout Processes ObservedData Posterior of FullData Parameters The Ignorability Assumption Examples of FullData Models under MAR FullData Models under MNAR Summary Further Reading Inference about FullData Parameters under Ignorability Overview General Issues in Model Specification Posterior Sampling Using Data Augmentation Covariance Structures for Univariate Longitudinal Processes CovariateDependent Covariance Structures Multivariate Processes Model Comparisons and Assessing Model Fit with Incomplete Data under Ignorability Further Reading Case Studies: Ignorable Missingness Overview Analysis of the Growth Hormone Study under MAR Analysis of the Schizophrenia Clinical Trial under MAR Using Random Effects Models Analysis of CTQ I Using Marginalized Transition Models under MAR Analysis of Weekly Smoking Outcomes in CTQ II Using Auxiliary Variable MAR Analysis of HERS CD4 Data under Ignorability Using Bayesian pSpline Models Summary Models for handling Nonignorable Missingness Overview Extrapolation Factorization Selection Models Mixture Models Shared Parameter Models Model Comparisons and Assessing Model Fit in Nonignorable Models Further Reading Informative Priors and Sensitivity Analysis Overview Some Principles Parameterizing the FullData Model PatternMixture Models Selection Models Elicitation of Expert Opinion, Construction of Informative Priors, and Formulation of Sensitivity Analyses A Note on Sensitivity Analysis in Fully Parametric Models Literature on Local Sensitivity Further Reading Case Studies: Model Specification and Data Analysis under Missing Not at Random Overview Analysis of Growth Hormone Study Using PatternMixture Models Analysis of OASIS Study Using Selection and PatternMixture Models Analysis of Pediatric AIDS Trial Using Mixture of Varying Coefficient Models Appendix: distributions Bibliography Index.
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QA276 .D3146 2008  Available 
15. Stability and sensitivity analysis for optimal control problems with controlstate constraints [2001]
 Malanowski, Kazimierz.
 Warszawa : Polska Akademia Nauk, Instytut Matematyczny, 2001.
 Description
 Book — 51 p. ; 24 cm.
 Online
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QA1 .D54 V.394  Available 
 Hoboken, NJ : Wiley, ©2004.
 Description
 Book — 1 online resource (xi, 219 pages) : illustrations
 Summary

 SENSITIVITY ANALYSIS IN PRACTICE
 CONTENTS
 PREFACE
 1 A WORKED EXAMPLE
 1.1 A simple model
 1.2 Modulus version of the simple model
 1.3 Sixfactor version of the simple model
 1.4 The simple model 'by groups'
 1.5 The (less) simple correlatedinput model
 1.6 Conclusions
 2 GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT
 2.1 Examples at a glance
 2.2 What is sensitivity analysis?
 2.3 Properties of an ideal sensitivity analysis method
 2.4 Defensible settings for sensitivity analysis
 2.5 Caveats
 3 TEST CASES
 3.1 The jumping man. Applying variancebased methods
 3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variancebased methods
 3.3 A model of fish population dynamics. Applying the method of Morris
 3.4 The Level E model. Radionuclide migration in the geosphere. Applying variancebased methods and Monte Carlo filtering
 3.5 Two spheres. Applying variance based methods in estimation/calibration problems
 3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems
 3.7 An analytical example. Applying the method of Morris
 4 THE SCREENING EXERCISE
 4.1 Introduction
 4.2 The method of Morris
 4.3 Implementing the method
 4.4 Putting the method to work: an analytical example
 4.5 Putting the method to work: sensitivity analysis of a fish population model
 4.6 Conclusions
 5 METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT
 5.1 The settings
 5.2 Factors Prioritisation Setting
 5.3 Firstorder effects and interactions
 5.4 Application of S(i) to Setting 'Factors Prioritisation'
 5.5 More on variance decompositions
 5.6 Factors Fixing (FF) Setting
 5.7 Variance Cutting (VC) Setting
 5.8 Properties of the variance based methods.
 5.9 How to compute the sensitivity indices: the case of orthogonal input
 5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST)
 5.10 How to compute the sensitivity indices: the case of nonorthogonal input
 5.11 Putting the method to work: the Level E model
 5.11.1 Case of orthogonal input factors
 5.11.2 Case of correlated input factors
 5.12 Putting the method to work: the bungee jumping model
 5.13 Caveats
 6 SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS
 6.1 Model calibration and Factors Mapping Setting
 6.2 Monte Carlo filtering and regionalised sensitivity analysis
 6.2.1 Caveats
 6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio
 6.4 Putting MC filtering and RSA to work: the Level E test case
 6.5 Bayesian uncertainty estimation and global sensitivity analysis
 6.5.1 Bayesian uncertainty estimation
 6.5.2 The GLUE case
 6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation
 6.5.4 Implementation of the method
 6.6 Putting Bayesian analysis and global SA to work: two spheres
 6.7 Putting Bayesian analysis and global SA to work: a chemical experiment
 6.7.1 Bayesian uncertainty analysis (GLUE case)
 6.7.2 Global sensitivity analysis
 6.7.3 Correlation analysis
 6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model
 6.8 Caveats
 7 HOW TO USE SIMLAB
 7.1 Introduction
 7.2 How to obtain and install SIMLAB
 7.3 SIMLAB main panel
 7.4 Sample generation
 7.4.1 FAST
 7.4.2 Fixed sampling
 7.4.3 Latin hypercube sampling (LHS)
 7.4.4 The method of Morris
 7.4.5 QuasiRandom LpTau
 7.4.6 Random
 7.4.7 Replicated Latin Hypercube (rLHS).
 7.4.8 The method of Sobol'
 7.4.9 How to induce dependencies in the input factors
 7.5 How to execute models
 7.6 Sensitivity analysis
 8 FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE
 REFERENCES
 INDEX.
 Naumann, Uwe, 1969
 Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 2011
 Description
 Book — 1 electronic text (xviii, 340 p.) : ill., digital file
 Summary

 Preface
 Acknowledgements
 Optimality
 1. Motivation and introduction
 2. First derivative code
 3. Higher derivative code
 4. Derivative code compilers  an introductory tutorial
 5. dcc  a prototype derivative code compiler
 Appendix A. Derivative code by overloading
 Appendix B. Syntax of dcc input
 Appendix C. (Hints on) Solutions
 Bibliography
 Index.
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 Cacuci, Dan Gabriel, author.
 Cham : Springer, [2022]
 Description
 Book — 1 online resource
 Summary

 Chapter 1: Introduction and Motivation: Breaking the Curse of Dimensionality in Sensitivity and Uncertainty Analysis Part A: FunctionValued Responses
 Chapter 2: The First and SecondOrder Comprehensive Adjoint Sensitivity Analysis Methodologies for Linear Systems with FunctionValued Responses 2.1. The FirstOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAM1) for Linear Systems with FunctionValued Responses 2.1.1: CSASAM1 Methodology: FiniteDimensional (Matrix) Systems 2.1.2: CSASAM1 Methodology: InfiniteDimensional (Operator) Systems 2.2. The SecondOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAM2) for Linear Systems with FunctionValued Responses 2.2.1: CSASAM2 Methodology: FiniteDimensional (Matrix) Systems 2.2.2: CSASAM2 Methodology: InfiniteDimensional (Operator) Systems 2.3. The FirstOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAM1) for Linear Systems with FunctionValued Responses 2.3.1: CKASAM1 Methodology: FiniteDimensional (Matrix) Systems 2.3.2: CKASAM1 Methodology: InfiniteDimensional (Operator) Systems 2.4. The SecondOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAM2) for Linear Systems with FunctionValued Responses 2.4.1: CKASAM2 Methodology: FiniteDimensional (Matrix) Systems 2.4.2: CKASAM2 Methodology: InfiniteDimensional (Operator) Systems
 Chapter 3: The ThirdOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM3) for Linear Systems with FunctionValued Responses 3.1. The ThirdOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAM3) for Linear Systems with FunctionValued Responses 3.1.1: CSASAM3 Methodology: FiniteDimensional (Matrix) Systems 3.1.2: CSASAM3 Methodology: InfiniteDimensional (Operator) Systems 3.2. The ThirdOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAM3) for Linear Systems with FunctionValued Responses 3.2.1: CKASAM3 Methodology: FiniteDimensional (Matrix) Systems 3.2.2: CKASAM3 Methodology: InfiniteDimensional (Operator) Systems
 Chapter 4: The FourthOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM4) for Linear Systems with FunctionValued Responses 4.1. The FourthOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAM4) for Linear Systems with FunctionValued Responses 4.1.1: CSASAM4 Methodology: FiniteDimensional (Matrix) Systems 4.1.2: CSASAM4 Methodology: InfiniteDimensional (Operator) Systems 4.2. The FourthOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAM4) for Linear Systems with FunctionValued Responses 4.2.1: CKASAM4 Methodology: FiniteDimensional (Matrix) Systems 4.2.2: CKASAM4 Methodology: InfiniteDimensional (Operator) Systems
 Chapter 5: The NthOrder Adjoint Sensitivity Analysis Methodology (CASAMN) for Linear Systems with FunctionValued Responses 5.1. The ArbitrarilyHigh NthOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAMN) for Linear Systems with FunctionValued Responses 5.1.1: CSASAMN Methodology: FiniteDimensional (Matrix) Systems 5.1.2: CSASAMN Methodology: InfiniteDimensional (Operator) Systems 5.2. The ArbitrarilyHigh NthOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAMN) for Linear Systems with FunctionValued Responses 5.2.1: CKASAMN Methodology: FiniteDimensional (Matrix) Systems 5.2.2: CKASAMN Methodology: InfiniteDimensional (Operator) Systems Part B: ScalarValued Responses
 Chapter 6: The FourthOrder Comprehensive Adjoint Sensitivity Analysis Methodology (CASAM4) for Linear Systems with ScalarValued Responses 6.1. The FourthOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAM4) for Linear Systems with ScalarValued Responses 6.1.1: CSASAM4 Methodology: FiniteDimensional (Matrix) Systems 6.1.2: CSASAM4 Methodology: InfiniteDimensional (Operator) Systems 6.2. The FourthOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAM4) for Linear Systems with ScalarValued Responses 6.2.1: CKASAM4 Methodology: FiniteDimensional (Matrix) Systems 6.2.2: CKASAM4 Methodology: InfiniteDimensional (Operator) Systems
 Chapter 7: The NthOrder Adjoint Sensitivity Analysis Methodology (CASAMN) for Linear Systems with ScalarValued Responses 7.1. The ArbitrarilyHigh NOrder Comprehensive Selective Adjoint Sensitivity Analysis Methodology (CSASAMN) for Linear Systems with ScalarValued Responses 7.1.1: CSASAMN Methodology: FiniteDimensional (Matrix) Systems 7.1.2: CSASAMN Methodology: InfiniteDimensional (Operator) Systems 7.2. The ArbitrarilyHigh NOrder Comprehensive Kernel Adjoint Sensitivity Analysis Methodology (CKASAMN) for Linear Systems with ScalarValued Responses 7.2.1: CKASAMN Methodology: FiniteDimensional (Matrix) Systems 7.2.2: CKASAMN Methodology: InfiniteDimensional (Operator) Systems.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Radhakrishnan, Krishnan.
 [Washington, D.C.] : [National Aeronautics and Space Administration], [1993]
 Description
 Book — 1 volume.
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.15:105851  Unknown 
 Cacuci, Dan Gabriel, author.
 First edition  Boca Raton, FL : Chapman and Hall/CRC, 2018
 Description
 Book — 1 online resource (326 pages) : 112 illustrations, text file, PDF
 Summary

 MOTIVATION FOR COMPUTING FIRST AND SECONDORDER SENSITIVITIES OF SYSTEM RESPONSES TO THE SYSTEMS PARAMETERS  The Fundamental Role of Response Sensitivities for Uncertainty Quantification  The Fundamental Role of Response Sensitivities for Predictive Modeling  Advantages and Disadvantages of Statistical and Deterministic Methods for Computing Response Sensitivities  ILLUSTRATIVE APPLICATION OF THE SECONDORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2ndASAM) TO A LINEAR EVOLUTION PROBLEM  Exact Computation of the 1stOrder Response Sensitivities  Exact Computation of the 2ndOrder Response Sensitivities  Computing the 2ndOrder Response Sensitivities Corresponding to the 1stOrder Sensitivities  Discussion of the Essential Features of the 2ndASAM  Illustrative Use of Response Sensitivities for Predictive Modeling  THE SECONDORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2ndASAM) FOR LINEAR SYSTEMS  Mathematical Modeling of a General Linear System  The 1stLevel Adjoint Sensitivity System (1stLASS) for Computing Exactly and Efficiently 1stOrder Sensitivities of ScalarValued Responses for Linear Systems  The 2ndLevel Adjoint Sensitivity System (2ndLASS) for Computing Exactly and Efficiently 1stOrder Sensitivities of ScalarValued Responses for Linear Systems  APPLICATION OF THE 2ndASAM TO A LINEAR HEAT CONDUCTION AND CONVECTION BENCHMARK PROBLEM  Heat Transport Benchmark Problem: Mathematical Modeling  Computation of FirstOrder Sensitivities Using the 2ndASAM  Computation of firstorder sensitivities of the heated rod temperature  Computation of firstorder sensitivities of the coolant temperature  Verification of the "ANSYS/FLUENT Adjoint Solver"  Applying the 2ndASAM to Compute the SecondOrder Sensitivities and Uncertainties for the Heat Transport Benchmark Problem  APPLICATION OF THE 2ndASAM TO A LINEAR PARTICLE DIFFUSION PROBLEM  Paradigm Diffusion Problem Description  Applying the 2ndASAM to Compute the FirstOrder Response Sensitivities to Model Parameters  Applying the 2ndASAM to Compute the SecondOrder Response Sensitivities to Model Parameters  Role of SecondOrder Response Sensitivities for Quantifying NonGaussian Features of the Response Uncertainty Distribution  Illustrative Application of FirstOrder Response Sensitivities for Predictive Modeling  APPLICATION OF THE 2ndASAM FOR COMPUTING SENSITIVITIES OF DETECTOR RESPONSES TO UNCOLLIDED RADIATION TRANSPORT  The RayTracing Form of the Forward and Adjoint Boltzmann Transport Equation  Application of the 2ndASAM to Compute the FirstOrder Response Sensitivities to Variations in Model Parameters  Application of the 2ndASAM to Compute the SecondOrder Response Sensitivities to Variations in Model Parameters  THE SECONDORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY (2ndASAM) FOR NONLINEAR SYSTEMS  Mathematical Modeling of a General Nonlinear System  The 1stLevel Adjoint Sensitivity System (1stLASS) for Computing Exactly and Efficiently the 1stOrder Sensitivities of ScalarValued Responses  The 2ndLevel Adjoint Sensitivity System (2ndLASS) for Computing Exactly and Efficiently the 2ndOrder Sensitivities of ScalarValued Responses for Nonlinear Systems  APPLICATION OF THE 2ndASAM TO A NONLINEAR HEAT CONDUCTION PROBLEM  Mathematical Modeling of Heated Cylindrical Test Section  Application of the 2ndASAM for Computing the 1stOrder Sensitivities  Application of the 2ndASAM for Computing the 2ndOrder Sensitivities
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