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Uncertainty Quantification And Predictive Computational Science
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Book Synopsis Uncertainty Quantification and Predictive Computational Science by : Ryan G. McClarren
Download or read book Uncertainty Quantification and Predictive Computational Science written by Ryan G. McClarren and published by Springer. This book was released on 2018-11-23 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.
Book Synopsis Uncertainty Quantification by : Ralph C. Smith
Download or read book Uncertainty Quantification written by Ralph C. Smith and published by SIAM. This book was released on 2013-12-02 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
Book Synopsis Uncertainty Quantification in Scientific Computing by : Andrew Dienstfrey
Download or read book Uncertainty Quantification in Scientific Computing written by Andrew Dienstfrey and published by Springer. This book was released on 2012-08-11 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-proceedings of the 10th IFIP WG 2.5 Working Conference on Uncertainty Quantification in Scientific Computing, WoCoUQ 2011, held in Boulder, CO, USA, in August 2011. The 24 revised papers were carefully reviewed and selected from numerous submissions. They are organized in the following topical sections: UQ need: risk, policy, and decision making, UQ theory, UQ tools, UQ practice, and hot topics. The papers are followed by the records of the discussions between the participants and the speaker.
Book Synopsis Uncertainty Quantification by : Christian Soize
Download or read book Uncertainty Quantification written by Christian Soize and published by Springer. This book was released on 2017-04-24 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available. This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.
Book Synopsis Introduction to Uncertainty Quantification by : T.J. Sullivan
Download or read book Introduction to Uncertainty Quantification written by T.J. Sullivan and published by Springer. This book was released on 2015-12-14 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study. Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
Book Synopsis Computational Uncertainty Quantification for Inverse Problems by : Johnathan M. Bardsley
Download or read book Computational Uncertainty Quantification for Inverse Problems written by Johnathan M. Bardsley and published by SIAM. This book was released on 2018-08-01 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The book also presents more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems. Each chapter contains MATLAB® code that implements the algorithms and generates the figures, as well as a large number of exercises accessible to both graduate students and researchers. Computational Uncertainty Quantification for Inverse Problems is intended for graduate students, researchers, and applied scientists. It is appropriate for courses on computational inverse problems, Bayesian methods for inverse problems, and UQ methods for inverse problems.
Book Synopsis Handbook of Uncertainty Quantification by : Roger Ghanem
Download or read book Handbook of Uncertainty Quantification written by Roger Ghanem and published by Springer. This book was released on 2016-05-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.
Book Synopsis Assessing the Reliability of Complex Models by : National Research Council
Download or read book Assessing the Reliability of Complex Models written by National Research Council and published by National Academies Press. This book was released on 2012-07-26 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners.
Book Synopsis Uncertainty Quantification in Computational Science by : Sunetra Sarkar
Download or read book Uncertainty Quantification in Computational Science written by Sunetra Sarkar and published by World Scientific Publishing Company. This book was released on 2016-07-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decade, research in Uncertainty Quantification (UC) has received a tremendous boost, in fluid engineering and coupled structural-fluids systems. New algorithms and adaptive variants have also emerged. This timely compendium overviews in detail the current state of the art of the field, including advances in structural engineering, along with the recent focus on fluids and coupled systems. Such a strong compilation of these vibrant research areas will certainly be an inspirational reference material for the scientific community.
Book Synopsis Uncertainty Quantification in Computational Fluid Dynamics by : Hester Bijl
Download or read book Uncertainty Quantification in Computational Fluid Dynamics written by Hester Bijl and published by Springer. This book was released on 2013-11-27 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.
Book Synopsis Cultures of Prediction in Atmospheric and Climate Science by : Matthias Heymann
Download or read book Cultures of Prediction in Atmospheric and Climate Science written by Matthias Heymann and published by Taylor & Francis. This book was released on 2017-06-26 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, science has experienced a revolutionary shift. The development and extensive application of computer modelling and simulation has transformed the knowledge‐making practices of scientific fields as diverse as astro‐physics, genetics, robotics and demography. This epistemic transformation has brought with it a simultaneous heightening of political relevance and a renewal of international policy agendas, raising crucial questions about the nature and application of simulation knowledges throughout public policy. Through a diverse range of case studies, spanning over a century of theoretical and practical developments in the atmospheric and environmental sciences, this book argues that computer modelling and simulation have substantially changed scientific and cultural practices and shaped the emergence of novel ‘cultures of prediction’. Making an innovative, interdisciplinary contribution to understanding the impact of computer modelling on research practice, institutional configurations and broader cultures, this volume will be essential reading for anyone interested in the past, present and future of climate change and the environmental sciences.
Book Synopsis Quantification of Uncertainty: Improving Efficiency and Technology by : Marta D'Elia
Download or read book Quantification of Uncertainty: Improving Efficiency and Technology written by Marta D'Elia and published by Springer Nature. This book was released on 2020-07-30 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.
Book Synopsis Uncertainty Quantification in Computational Science by : Sunetra Sarkar
Download or read book Uncertainty Quantification in Computational Science written by Sunetra Sarkar and published by World Scientific. This book was released on 2016-08-19 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decade, research in Uncertainty Quantification (UC) has received a tremendous boost, in fluid engineering and coupled structural-fluids systems. New algorithms and adaptive variants have also emerged. This timely compendium overviews in detail the current state of the art of the field, including advances in structural engineering, along with the recent focus on fluids and coupled systems. Such a strong compilation of these vibrant research areas will certainly be an inspirational reference material for the scientific community.
Book Synopsis Uncertainty Quantification in Multiscale Materials Modeling by : Yan Wang
Download or read book Uncertainty Quantification in Multiscale Materials Modeling written by Yan Wang and published by Woodhead Publishing Limited. This book was released on 2020-03-12 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
Book Synopsis 14th International Symposium on Process Systems Engineering by : Yoshiyuki Yamashita
Download or read book 14th International Symposium on Process Systems Engineering written by Yoshiyuki Yamashita and published by Elsevier. This book was released on 2022-06-24 with total page 2304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 14th International Symposium on Process Systems Engineering, Volume 49 brings together the international community of researchers and engineers interested in computing-based methods in process engineering. The conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 2021 event held in Tokyo, Japan, July 1-23, 2021. It contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and covering future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. Highlights how the Process Systems Engineering community contributes to the sustainability of modern society Establishes the core products of Process Systems Engineering Defines the future challenges of Process Systems Engineering
Book Synopsis Computational Nuclear Engineering and Radiological Science Using Python by : Ryan McClarren
Download or read book Computational Nuclear Engineering and Radiological Science Using Python written by Ryan McClarren and published by Academic Press. This book was released on 2017-10-27 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Nuclear Engineering and Radiological Science Using Python provides the necessary knowledge users need to embed more modern computing techniques into current practices, while also helping practitioners replace Fortran-based implementations with higher level languages. The book is especially unique in the market with its implementation of Python into nuclear engineering methods, seeking to do so by first teaching the basics of Python, then going through different techniques to solve systems of equations, and finally applying that knowledge to solve problems specific to nuclear engineering. Along with examples of code and end-of-chapter problems, the book is an asset to novice programmers in nuclear engineering and radiological sciences, teaching them how to analyze complex systems using modern computational techniques. For decades, the paradigm in engineering education, in particular, nuclear engineering, has been to teach Fortran along with numerical methods for solving engineering problems. This has been slowly changing as new codes have been written utilizing modern languages, such as Python, thus resulting in a greater need for the development of more modern computational skills and techniques in nuclear engineering. Offers numerical methods as a tool to solve specific problems in nuclear engineering Provides examples on how to simulate different problems and produce graphs using Python Supplies accompanying codes and data on a companion website, along with solutions to end-of-chapter problems
Book Synopsis Recent Advancements in Multi-View Data Analytics by : Witold Pedrycz
Download or read book Recent Advancements in Multi-View Data Analytics written by Witold Pedrycz and published by Springer Nature. This book was released on 2022-05-20 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others. The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.
