Sensitivity and Uncertainty Analysis of Complex Simulation Models
Author: Szu Hui Ng
Publisher:
Published: 2001
Total Pages: 398
ISBN-13:
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Water Resource Systems Planning and Management
Author: Daniel P. Loucks
Publisher: Springer
Published: 2017-03-02
Total Pages: 635
ISBN-13: 3319442341
DOWNLOAD EBOOKThis book is open access under a CC BY-NC 4.0 license. This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.
Sensitivity & Uncertainty Analysis, Volume 1
Author: Dan G. Cacuci
Publisher: CRC Press
Published: 2003-05-28
Total Pages: 304
ISBN-13: 0203498798
DOWNLOAD EBOOKAs computer-assisted 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
Sensitivity and Uncertainty Analysis, Volume II
Author: Dan G. Cacuci
Publisher: CRC Press
Published: 2005-05-16
Total Pages: 367
ISBN-13: 020348357X
DOWNLOAD EBOOKAs computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative convective model for climate simulations, and large-scale models for numerical weather prediction.
Uncertainty and Sensitivity Analysis for Long-running Computer Codes
Author: Dustin R. Langewisch
Publisher:
Published: 2010
Total Pages: 158
ISBN-13:
DOWNLOAD EBOOKThis thesis presents a critical review of existing methods for performing probabilistic uncertainty and sensitivity analysis for complex, computationally expensive simulation models. Uncertainty analysis (UA) methods reviewed include standard Monte Carlo simulation, Latin Hypercube sampling, importance sampling, line sampling, and subset simulation. Sensitivity analysis (SA) methods include scatter plots, Monte Carlo filtering, regression analysis, variance-based methods (Sobol' sensitivity indices and Sobol' Monte Carlo algorithms), and Fourier amplitude sensitivity tests. In addition, this thesis reviews several existing metamodeling techniques that are intended provide quick-running approximations to the computer models being studied. Because stochastic simulation-based UA and SA rely on a large number (e.g., several thousands) of simulations, metamodels are recognized as a necessary compromise when UA and SA must be performed with long-running (i.e., several hours or days per simulation) computational models. This thesis discusses the use of polynomial Response Surfaces (RS), Artificial Neural Networks (ANN), and Kriging/Gaussian Processes (GP) for metamodeling. Moreover, two methods are discussed for estimating the uncertainty introduced by the metamodel. The first of these methods is based on a bootstrap sampling procedure, and can be utilized for any metamodeling technique.
Sensitivity and Uncertainty Analysis of Complex Dynamical Models
Author: Sara Abdelmageed
Publisher:
Published: 2011
Total Pages: 89
ISBN-13:
DOWNLOAD EBOOKGlobal Sensitivity Analysis
Author: Andrea Saltelli
Publisher: John Wiley & Sons
Published: 2008-02-28
Total Pages: 304
ISBN-13: 9780470725177
DOWNLOAD EBOOKComplex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
Sensitivity Analysis in Practice
Author: Andrea Saltelli
Publisher: John Wiley & Sons
Published: 2004-07-16
Total Pages: 232
ISBN-13: 047087094X
DOWNLOAD EBOOKSensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.
Sensitivity & Uncertainty Analysis
Author: Dan G. Cacuci
Publisher: Chapman and Hall/CRC
Published: 2003-05-28
Total Pages: 304
ISBN-13: 9781584881155
DOWNLOAD EBOOKAs computer-assisted 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.
Uncertainty Analysis with High Dimensional Dependence Modelling
Author: Dorota Kurowicka
Publisher: John Wiley & Sons
Published: 2006-10-02
Total Pages: 302
ISBN-13: 0470863080
DOWNLOAD EBOOKMathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.
