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Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty (Springer Theses)
by Vassilis M. CharitopoulosThis book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.
Uncertainty, Expectations and Asset Price Dynamics: Essays in Honor of Georges Prat (Dynamic Modeling and Econometrics in Economics and Finance #24)
by Fredj JawadiWritten in honor of Emeritus Professor Georges Prat (University of Paris Nanterre, France), this book includes contributions from eminent authors on a range of topics that are of interest to researchers and graduates, as well as investors and portfolio managers. The topics discussed include the effects of information and transaction costs on informational and allocative market efficiency, bubbles and stock price dynamics, paradox of rational expectations and the principle of limited information, uncertainty and expectation hypotheses, oil price dynamics, and nonlinearity in asset price dynamics.
Uncertainty in Biology
by Liesbet Geris David Gomez-CabreroComputational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
Uncertainty in Complex Networked Systems: In Honor of Roberto Tempo (Systems & Control: Foundations & Applications)
by Tamer BaşarThe chapters in this volume, and the volume itself, celebrate the life and research of Roberto Tempo, a leader in the study of complex networked systems, their analysis and control under uncertainty, and robust designs. Contributors include authorities on uncertainty in systems, robustness, networked and network systems, social networks, distributed and randomized algorithms, and multi-agent systems—all fields that Roberto Tempo made vital contributions to. Additionally, at least one author of each chapter was a research collaborator of Roberto Tempo’s.This volume is structured in three parts. The first covers robustness and includes topics like time-invariant uncertainties, robust static output feedback design, and the uncertainty quartet. The second part is focused on randomization and probabilistic methods, which covers topics such as compressive sensing, and stochastic optimization. Finally, the third part deals with distributed systems and algorithms, and explores matters involving mathematical sociology, fault diagnoses, and PageRank computation.Each chapter presents exposition, provides new results, and identifies fruitful future directions in research. This book will serve as a valuable reference volume to researchers interested in uncertainty, complexity, robustness, optimization, algorithms, and networked systems.
Uncertainty in Engineering: Introduction to Methods and Applications (SpringerBriefs in Statistics)
by Louis J. M. Aslett Frank P. A. Coolen Jasper De BockThis 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.
Uncertainty in Risk Assessment
by Terje Aven Roger Flage Enrico Zio Piero BaraldiExplores methods for the representation and treatment of uncertainty in risk assessmentIn providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications.While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences.Uncertainty in Risk Assessment:Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts.Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory.Offers guidance on when to use probability and when to use an alternative representation of uncertainty.Presents and discusses methods for the representation and characterization of uncertainty in risk assessment.Uses examples to clearly illustrate ideas and concepts.
Uncertainty Management for Robust Industrial Design in Aeronautics: Findings and Best Practice Collected During UMRIDA, a Collaborative Research Project (2013–2016) Funded by the European Union (Notes on Numerical Fluid Mechanics and Multidisciplinary Design #140)
by Charles Hirsch Dirk Wunsch Jacek Szumbarski Łukasz Łaniewski-Wołłk Jordi Pons-PratsThis book covers cutting-edge findings related to uncertainty quantification and optimization under uncertainties (i.e. robust and reliable optimization), with a special emphasis on aeronautics and turbomachinery, although not limited to these fields. It describes new methods for uncertainty quantification, such as non-intrusive polynomial chaos, collocation methods, perturbation methods, as well as adjoint based and multi-level Monte Carlo methods. It includes methods for characterization of most influential uncertainties, as well as formulations for robust and reliable design optimization. A distinctive element of the book is the unique collection of test cases with prescribed uncertainties, which are representative of the current engineering practice of the industrial consortium partners involved in UMRIDA, a level 1 collaborative project within the European Commission's Seventh Framework Programme (FP7). All developed methods are benchmarked against these industrial challenges. Moreover, the book includes a section dedicated to Best Practice Guidelines for uncertainty quantification and robust design optimization, summarizing the findings obtained by the consortium members within the UMRIDA project. All in all, the book offers a authoritative guide to cutting-edge methodologies for uncertainty management in engineering design, covers a wide range of applications and discusses new ideas for future research and interdisciplinary collaborations.
Uncertainty Quantification and Predictive Computational Science: A Foundation for Physical Scientists and Engineers
by Ryan G. McClarrenThis 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.
Uncertainty Quantification and Stochastic Modelling with EXCEL (Springer Texts in Business and Economics)
by Eduardo Souza de CursiThis book presents techniques for determining uncertainties in numerical solutions with applications in the fields of business administration, civil engineering, and economics, using Excel as a computational tool. Also included are solutions to uncertainty problems involving stochastic methods. The list of topics specially covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi objective optimization, and Game Theory, as well as linear algebraic equations, and probability and statistics. The book also provides a selection of numerical methods developed for Excel, in order to enhance readers’ understanding. As such, it offers a valuable guide for all graduate and undergraduate students in the fields of economics, business administration, civil engineering, and others that rely on Excel as a research tool.
Uncertainty Quantification for Hyperbolic and Kinetic Equations (SEMA SIMAI Springer Series #14)
by Shi Jin Lorenzo PareschiThis book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods. The interest in these topics is rapidly growing, as their applications have now expanded to many areas in engineering, physics, biology and the social sciences. Accordingly, the book provides the scientific community with a topical overview of the latest research efforts.
Uncertainty Quantification in Laminated Composites: A Meta-model Based Approach
by Sudip Dey Tanmoy Mukhopadhyay Sondipon AdhikariOver the last few decades, uncertainty quantification in composite materials and structures has gained a lot of attention from the research community as a result of industrial requirements. This book presents computationally efficient uncertainty quantification schemes following meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. Several metamodels have been studied and comparative results have been presented for different static and dynamic responses. Results for sensitivity analyses are provided for a comprehensive coverage of the relative importance of different material and geometric parameters in the global structural responses.
Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications
by Akhtar A. Khan Joachim Gwinner Baasansuren Jadamba Fabio RacitiUncertainty Quantification (UQ) is an emerging and extremely active research discipline which aims to quantitatively treat any uncertainty in applied models. The primary objective of Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications is to present a comprehensive treatment of UQ in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields. Features First book on UQ in variational inequalities emerging from various network, economic, and engineering models Completely self-contained and lucid in style Aimed for a diverse audience including applied mathematicians, engineers, economists, and professionals from academia Includes the most recent developments on the subject which so far have only been available in the research literature
Uncertainty Quantification using R (International Series in Operations Research & Management Science #335)
by Eduardo Souza de CursiThis book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.
Uncertainty Quantification with R: Bayesian Methods (International Series in Operations Research & Management Science #352)
by Eduardo Souza de CursiThis book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of Bayesian uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems.The list of topics covered in this volume includes basic Bayesian probabilities, entropy, Bayesian estimation and decision, sequential Bayesian estimation, and numerical methods. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of Bayesian uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.
Uncovering Student Thinking About Mathematics in the Common Core, Grades 3-5: 25 Formative Assessment Probes
by Cheryl Rose Tobey Emily R. FaganTake the guesswork out of grades 3-5 math assessment! Quickly pinpoint and reverse your students’ common math difficulties with this detailed and easy-to-follow resource from best-selling authors Cheryl Tobey and Carolyn Arline. Twenty research-based assessment probes help you ask the right questions to uncover just where your students get confused – while learning is already underway. These CCSM-aligned probes eliminate all guesswork and will help you: Systematically address conceptual and procedural mistakes Plan targeted instruction and remediation in multiplication and division, problem solving, the four operations, factorization, and beyond Master essential CCSM mathematical processes and proficiencies for Grades 3-5
Uncovering Student Thinking About Mathematics in the Common Core, Grades 6-8: 25 Formative Assessment Probes
by Cheryl Rose Tobey Carolyn B. ArlinePinpoint and reverse math misconceptions with laser-like accuracy Quickly and reliably uncover common math misconceptions in Grades 6-8 with these convenient and easy-to-implement diagnostic tools! Bestselling authors Cheryl Tobey and Carolyn Arline provide 25 new assessment probes that pinpoint subconcepts within the new Common Core Standards for Mathematics to promote deep learning and expert math instruction--while learning is already underway. Completely CCSM aligned, these grade-specific probes eliminate the guesswork and help teachers: Systematically address conceptual and procedural mistakes Help students better understand areas of struggle Plan targeted instruction that covers Grades 6-8 CCSM mathematical processes and proficiencies
Uncovering Student Thinking About Mathematics in the Common Core, Grades K–2: 20 Formative Assessment Probes
by Cheryl Rose Tobey Emily R. FaganGet to the core of your students’ understanding of math! Quickly and reliably identify your primary students’ math knowledge with these convenient and easy-to-implement diagnostic tools! Tobey and Fagan provide 25 new assessments specifically for Grades K–2 and directly aligned with the Common Core. Organized by strand, the probes will enable you to: Quickly and objectively evaluate each child’s prior knowledge of basic math and numeracy Systematically address common mistakes and obstacles before they become long-term problems Make sound instructional choices to improve all students’ math skills
Uncovering Student Thinking About Mathematics in the Common Core, High School: 25 Formative Assessment Probes
by Cheryl Rose Tobey Carolyn B. ArlineTake the guesswork out of high school math instruction! Quickly and reliably uncover common math misconceptions in Grades 9-12 with these convenient and easy-to-implement diagnostic tools! Bestselling authors Cheryl Rose Tobey and Carolyn B. Arline provide 25 new assessment probes that pinpoint subconcepts within the Common Core State Standards to promote deep learning and expert math instruction—all while learning is underway. Completely Common Core aligned, these grade-specific probes eliminate the guesswork and will help you Systematically address conceptual and procedural mistakes Pinpoint where students are struggling Plan targeted instruction in algebra, functions, logarithms, geometry, trigonometric ratios, statistics and probability, and more
Uncovering Student Thinking in Mathematics: 25 Formative Assessment Probes
by Cheryl Rose Tobey Leslie G. Minton Carolyn B. ArlineAppropriate for all grade levels, these 25 field-tested, easy-to-use mathematics assessment probes help teachers modify instruction by determining students' understanding of core mathematical concepts.
Uncovering Student Thinking in Mathematics, Grades 6-12: 30 Formative Assessment Probes for the Secondary Classroom
by Cheryl Rose Tobey Carolyn B. ArlineDiscussing standards, research, and more, these 30 probes help secondary teachers assess students' grasp of core mathematics concepts and modify their instruction to improve student achievement.
Uncovering Student Thinking in Mathematics, Grades K-5: 25 Formative Assessment Probes for the Elementary Classroom
by Cheryl Rose Tobey Leslie G. MintonThis book provides 25 easily administered assessments of learners' math knowledge that help teachers monitor learning in real time and improve all students' math skills.
Undecidability, Uncomputability, and Unpredictability (The Frontiers Collection)
by Anthony Aguirre Zeeya Merali David SloanFor a brief time in history, it was possible to imagine that a sufficiently advanced intellect could, given sufficient time and resources, in principle understand how to mathematically prove everything that was true. They could discern what math corresponds to physical laws, and use those laws to predict anything that happens before it happens. That time has passed. Gödel’s undecidability results (the incompleteness theorems), Turing’s proof of non-computable values, the formulation of quantum theory, chaos, and other developments over the past century have shown that there are rigorous arguments limiting what we can prove, compute, and predict. While some connections between these results have come to light, many remain obscure, and the implications are unclear. Are there, for example, real consequences for physics — including quantum mechanics — of undecidability and non-computability? Are there implications for our understanding of the relations between agency, intelligence, mind, and the physical world? This book, based on the winning essays from the annual FQXi competition, contains ten explorations of Undecidability, Uncomputability, and Unpredictability. The contributions abound with connections, implications, and speculations while undertaking rigorous but bold and open-minded investigation of the meaning of these constraints for the physical world, and for us as humans.
Under the Sky We Make: How to Be Human in a Warming World
by Kimberly NicholasIt's warming. It's us. We're sure. It's bad. But we can fix it.After speaking to the international public for close to fifteen years about sustainability, climate scientist Dr. Nicholas realized that concerned people were getting the wrong message about the climate crisis. Yes, companies and governments are hugely responsible for the mess we're in. But individuals CAN effect real, significant, and lasting change to solve this problem. Nicholas explores finding purpose in a warming world, combining her scientific expertise and her lived, personal experience in a way that seems fresh and deeply urgent: Agonizing over the climate costs of visiting loved ones overseas, how to find low-carbon love on Tinder, and even exploring her complicated family legacy involving supermarket turkeys.In her astonishing book Under the Sky We Make, Nicholas does for climate science what Michael Pollan did more than a decade ago for the food on our plate: offering a hopeful, clear-eyed, and somehow also hilarious guide to effecting real change, starting in our own lives. Saving ourselves from climate apocalypse will require radical shifts within each of us, to effect real change in our society and culture. But it can be done. It requires, Dr. Nicholas argues, belief in our own agency and value, alongside a deep understanding that no one will ever hand us power--we're going to have to seize it for ourselves.
The Undercount of Young Children in the U.S. Decennial Census
by William P. O'HareThis book covers several dimensions of the undercount of young children in the U. S. Decennial Census, examines the data from the 2010 U. S. Decennial Census in detail and looks at trends in the undercount of children over time. Other aspects included are the geographic distribution of the net undercount and an exploration for some of the potential explanations for the high net undercount of children. The number of young children in the US is growing, but almost one million young children (under age 5) were missed in the 2010 U. S. Decennial Census. The net undercount of young children has been higher than any other age group for the past several decades and is increasing rapidly, but little attention has been paid to the issue but demographers or the public.
Undergraduate Algebra: A Unified Approach (Springer Undergraduate Mathematics Series)
by Matej BrešarThis textbook offers an innovative approach to abstract algebra, based on a unified treatment of similar concepts across different algebraic structures. This makes it possible to express the main ideas of algebra more clearly and to avoid unnecessary repetition.The book consists of two parts: The Language of Algebra and Algebra in Action. The unified approach to different algebraic structures is a primary feature of the first part, which discusses the basic notions of algebra at an elementary level. The second part is mathematically more complex, covering topics such as the Sylow theorems, modules over principal ideal domains, and Galois theory.Intended for an undergraduate course or for self-study, the book is written in a readable, conversational style, is rich in examples, and contains over 700 carefully selected exercises.