Download or read book PREDICTIVE GLOBAL SENSITIVITY ANALYSIS written by CHARLES L.. LUO MUNSON (LAN. HUANG, XIAOHUI.) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex 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.

Download or read book Global Sensitivity Analysis Probabilistic Calibration and Predictive Assessment for the Data Assimilation Linked Ecosystem Carbon Model written by and published by . This book was released on 2015 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.

Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity 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.

Download or read book Sensitivity Analysis written by Emanuele Borgonovo and published by Springer. This book was released on 2017-04-19 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques for performing their sensitivity analysis. A variety of sensitivity methods have been developed over the years. This monograph helps the analyst in her/his first exploration of this world. The main goal is to foster the recognition of the crucial role of sensitivity analysis methods as the techniques that allow us to gain insights from quantitative models. Also, exercising rigor in performing sensitivity analysis becomes increasingly relevant both to decision makers and modelers. The book helps the analyst in structuring her/his sensitivity analysis quest properly, so as to obtain the correct answer to the corresponding managerial question. The first part of the book covers Deterministic Methods, including Tornado Diagrams; One-Way Sensitivity Analysis; Differentiation-Based Methods and Local Sensitivity Analysis with Constraints. The second part looks at Probabilistic Methods, including Regression-Based methods, Variance-Based Methods, and Distribution-Based methods. The final section looks at Applications, including capital budgeting, sensitivity analysis in climate change modelling and in the risk assessment of a lunar space mission.

Download or read book Uncertainty Quantification and Sensitivity Analysis of Geoscientific Predictions with Data driven Approaches written by Jihoon Park and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty quantification in the Earth Sciences forms an integral component in decision making. Such decision has different objectives depending on the subsurface system. For example, the goals include maximizing profits in exploitation of resources or minimizing the effects on the environment. It is often the case that the decision has to balance between multiple conflicting objectives. Because the decision is made on prediction uncertainty, it is crucial to quantify realistic uncertainty which necessitates identification of a variety of sources of model uncertainty. The sources of model uncertainty include different interpretations on subsurface structures and depositional scenarios, unknown spatial distributions of properties, uncertainty in boundary conditions, hydrological/hydraulic properties and errors in measurements. The subsurface system is parameterized to represent model uncertainty. The model variable can be either global (takes scalar value) or spatially distributed. With limited available data, a large number of uncertain model variables exists. One of key tasks is to quantify how each model variable contribute to response uncertainty, which can be achieved by means of sensitivity analysis. Sensitivity analysis plays an important role in geoscientific computer experiments, whether for forecasting, data assimilation or model calibration. Some methods of sensitivity analysis have been used in Earth Sciences but they have clear limitations -- they cannot efficiently deal with multivariate responses, excessive calculations are required, and it is hard to take into account categorical input uncertainty. Overcoming these limitations, we revisit the idea of regionalized sensitivity analysis. In particular, we focus on distance-based global sensitivity analysis to estimate sensitivities of multivariate responses with limited number of samples. We demonstrate how the results from sensitivity analysis can be utilized to reduce model uncertainty with minimal impact on response uncertainty. The results can be used to design second Monte Carlo or building a surrogate model. Uncertainty needs to be updated as more data are required from different sources. In a Bayesian framework, this requires sampling from a posterior density of model and prediction variables. The key components of the workflow are dimensionality reduction of data variables and building of a statistical surrogate model to replace full forward models. It is demonstrated that the methodology successfully performs model inversions with limited number of full forward model runs. In many geoscientific applications, both global and spatial variables are uncertain. For convenience in computations, spatial variables are often converted to a few global variables. Even if the approach is efficient, the inversion results may not be consistent with the stated geological prior which leads to unrealistic uncertainty. In this dissertation, we propose to extend direct forecasting to predict model variables themselves. It is shown that successful inversion can be performed with both global and spatial variables characterizing a field-scale subsurface system. All the methodologies are demonstrated with the case studies. The first case deals with an oil reservoir in Libya. The case is used to study the proposed methods for global sensitivity analysis and approaches for model inversions to integrate dynamic data. The second case deals with the groundwater reservoir in Denmark. The case is used to integrate different sources of data to offer the inputs of decision models for groundwater management.

Download or read book Sensitivity Analysis in Earth Observation Modelling written by George P. Petropoulos and published by Elsevier. This book was released on 2016-10-07 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling. Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement. Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling Provides readers with a roadmap for directing future efforts Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow

Download or read book Sensitivity and Uncertainty Analysis Volume II written by Dan G. Cacuci and published by CRC Press. This book was released on 2005-05-16 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: As 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.

Download or read book Sensitivity Analysis written by Andrea Saltelli and published by Wiley. This book was released on 2009-03-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists. Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice and is an implicit part of any modelling field. Offers an accessible introduction to sensitivity analysis. Covers all the latest research. Illustrates concepts with numerous examples, applications and case studies. Includes contributions from the leading researchers active in developing strategies for sensitivity analysis. The principles of sensitivity analysis are carefully described and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire casual assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A ‘hitch-hikers guide’ is included to allow the more experienced reader to readily access specific applications. Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly from the numerous examples and applications. "Presents many different sensitivity analysis methodologies and demonstrates their usefulness in scientific research." (Zentralblatt MATH)

Download or read book Advances in Materials and Pavement Performance Prediction II written by K. Anupam and published by CRC Press. This book was released on 2020-12-08 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: - rigid pavements - pavement geotechnics - statistical and data tools in pavement engineering - pavement structures - asphalt mixtures - asphalt binders The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.

Download or read book Decision and Prediction Analysis Powered With Operations Research written by Bubevski, Vojo and published by IGI Global. This book was released on 2024-07-16 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organizations today face complex decisions and uncertainties that can have a profound impact on their financial stability and strategic direction. Traditional decision-making methods often fall short when it comes to addressing multifaceted issues like financing, product manufacturing, and facility location. These challenges demand a robust framework that quantifies factors, assesses risks, and provides optimal solutions. Without advanced tools and techniques, businesses are at risk of making uninformed decisions that could lead to significant financial losses and missed opportunities. The urgency to equip yourself with these tools is clear. Decision and Prediction Analysis Powered With Operations Research offers a comprehensive solution to these challenges. This book integrates operations research techniques to reframe and solve complex business problems. It provides a detailed exploration of decision analysis tools, such as influence diagrams and decision trees, which help visualize and assess various decision scenarios. By applying these tools, organizations can better understand uncertainties, evaluate risks, and make decisions that maximize expected utility and achieve strategic objectives.

Download or read book Sensitivity Analysis and Goal Oriented Error Estimation for Model Predictive Control written by Manuel Schaller and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Model Validation and Uncertainty Quantification Volume 3 written by H. Sezer Atamturktur and published by Springer. This book was released on 2015-04-25 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Validation and Uncertainty Quantification, Volume 3. Proceedings of the 33rd IMAC, A Conference and Exposition on Balancing Simulation and Testing, 2015, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Uncertainty Quantification & Model Validation Uncertainty Propagation in Structural Dynamics Bayesian & Markov Chain Monte Carlo Methods Practical Applications of MVUQ Advances in MVUQ & Model Updating

Download or read book Sensitivity analysis and predictive uncertainty of the Institute of Hydrology Distributed Model written by C C M. Rogers and published by . This book was released on 1985 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Uncertainty in Engineering written by Louis J. M. Aslett and published by Springer Nature. This book was released on 2022 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.

Download or read book Model form Uncertainty Quantification for Predictive Probabilistic Graphical Models written by Jinchao Feng and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we focus on Uncertainty Quantification and Sensitivity Analysis, which can provide performance guarantees for predictive models built with both aleatoric and epistemic uncertainties, as well as data, and identify which components in a model have the most influence on predictions of our quantities of interest. In the first part (Chapter 2), we propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depend critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated. In the second part (Chapter 3-4), we develop new information-based uncertainty quantification and sensitivity analysis methods for Probabilistic Graphical Models. Probabilistic graphical models are an important class of methods for probabilistic modeling and inference, probabilistic machine learning, and probabilistic artificial intelligence. Its hierarchical structure allows us to bring together in a systematic way statistical and multi-scale physical modeling, different types of data, incorporating expert knowledge, correlations, and causal relationships. However, due to multi-scale modeling, learning from sparse data, and mechanisms without full knowledge, many predictive models will necessarily have diverse sources of uncertainty at different scales. The new model-form uncertainty quantification indices we developed can handle both parametric and non-parametric probabilistic graphical models, as well as small and large model/parameter perturbations in a single, unified mathematical framework and provide an envelope of model predictions for our quantities of interest. Moreover, we propose a model-form Sensitivity Index, which allows us to rank the impact of each component of the probabilistic graphical model, and provide a systematic methodology to close the experiment - model - simulation - prediction loop and improve the computational model iteratively based on our new uncertainty quantification and sensitivity analysis methods. To illustrate our ideas, we explore a physicochemical application on the Oxygen Reduction Reaction (ORR) in Chapter 4, whose optimization was identified as a key to the performance of fuel cells. In the last part (Chapter 5), we complete our discussion for the uncertainty quantification and sensitivity analysis methods on probabilistic graphical models by introducing a new sensitivity analysis method for the case where we know the real model sits in a certain parametric family. Note that the uncertainty indices above may be too pessimistic (as they are inherently non-parametric) when studying uncertainty/sensitivity questions for models confined within a given parametric family. Therefore, we develop a method using likelihood ratio and fisher information matrix, which can capture correlations and causal dependencies in the graphical models, and we show it can provide us more accurate results for the parametric probabilistic graphical models.

Download or read book Global Sensitivity Analysis TRACE Model Data with Deep Neural Network Based Surrogate Models written by Halil Ibrahim San and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: