Modeling, Design, and Simulation of Systems with Uncertainties

Download or Read eBook Modeling, Design, and Simulation of Systems with Uncertainties PDF written by Andreas Rauh and published by Springer Science & Business Media. This book was released on 2011-06-06 with total page 356 pages. Available in PDF, EPUB and Kindle.
Modeling, Design, and Simulation of Systems with Uncertainties

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Publisher: Springer Science & Business Media

Total Pages: 356

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ISBN-10: 9783642159565

ISBN-13: 3642159567

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Book Synopsis Modeling, Design, and Simulation of Systems with Uncertainties by : Andreas Rauh

To describe the true behavior of most real-world systems with sufficient accuracy, engineers have to overcome difficulties arising from their lack of knowledge about certain parts of a process or from the impossibility of characterizing it with absolute certainty. Depending on the application at hand, uncertainties in modeling and measurements can be represented in different ways. For example, bounded uncertainties can be described by intervals, affine forms or general polynomial enclosures such as Taylor models, whereas stochastic uncertainties can be characterized in the form of a distribution described, for example, by the mean value, the standard deviation and higher-order moments. The goal of this Special Volume on Modeling, Design, and Simulation of Systems with Uncertainties is to cover modern methods for dealing with the challenges presented by imprecise or unavailable information. All contributions tackle the topic from the point of view of control, state and parameter estimation, optimization and simulation. Thematically, this volume can be divided into two parts. In the first we present works highlighting the theoretic background and current research on algorithmic approaches in the field of uncertainty handling, together with their reliable software implementation. The second part is concerned with real-life application scenarios from various areas including but not limited to mechatronics, robotics, and biomedical engineering.

Uncertainty Quantification in Multiscale Materials Modeling

Download or Read eBook Uncertainty Quantification in Multiscale Materials Modeling PDF 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.
Uncertainty Quantification in Multiscale Materials Modeling

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Publisher: Woodhead Publishing Limited

Total Pages: 604

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ISBN-10: 9780081029411

ISBN-13: 0081029411

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Book Synopsis Uncertainty Quantification in Multiscale Materials Modeling by : Yan Wang

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.

Numerical Simulation-based Design

Download or Read eBook Numerical Simulation-based Design PDF written by Xu Han and published by . This book was released on 2020 with total page 258 pages. Available in PDF, EPUB and Kindle.
Numerical Simulation-based Design

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Total Pages: 258

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ISBN-10: 981103091X

ISBN-13: 9789811030918

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Book Synopsis Numerical Simulation-based Design by : Xu Han

This book focuses on numerical simulation-based design theory and methods in mechanical engineering. The simulation-based design of mechanical equipment involves considerable scientific challenges including extremely complex systems, extreme working conditions, multi-source uncertainties, multi-physics coupling, and large-scale computation. In order to overcome these technical difficulties, this book systematically elaborates upon the advanced design methods, covering high-fidelity simulation modeling, rapid structural analysis, multi-objective design optimization, uncertainty analysis and optimization, which can effectively improve the design accuracy, efficiency, multi-functionality and reliability of complicated mechanical structures. This book is primarily intended for researchers, engineers and postgraduate students in mechanical engineering, especially in mechanical design, numerical simulation and engineering optimization.

Uncertainty Management in Simulation-Optimization of Complex Systems

Download or Read eBook Uncertainty Management in Simulation-Optimization of Complex Systems PDF written by Gabriella Dellino and published by Springer. This book was released on 2015-06-29 with total page 271 pages. Available in PDF, EPUB and Kindle.
Uncertainty Management in Simulation-Optimization of Complex Systems

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Publisher: Springer

Total Pages: 271

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ISBN-10: 9781489975478

ISBN-13: 1489975470

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Book Synopsis Uncertainty Management in Simulation-Optimization of Complex Systems by : Gabriella Dellino

​This book aims at illustrating strategies to account for uncertainty in complex systems described by computer simulations. When optimizing the performances of these systems, accounting or neglecting uncertainty may lead to completely different results; therefore, uncertainty management is a major issues in simulation-optimization. Because of its wide field of applications, simulation-optimization issues have been addressed by different communities with different methods, and from slightly different perspectives. Alternative approaches have been developed, also depending on the application context, without any well-established method clearly outperforming the others. This editorial project brings together — as chapter contributors — researchers from different (though interrelated) areas; namely, statistical methods, experimental design, stochastic programming, global optimization, metamodeling, and design and analysis of computer simulation experiments. Editors’ goal is to take advantage of such a multidisciplinary environment, to offer to the readers a much deeper understanding of the commonalities and differences of the various approaches to simulation-based optimization, especially in uncertain environments. Editors aim to offer a bibliographic reference on the topic, enabling interested readers to learn about the state-of-the-art in this research area, also accounting for potential real-world applications to improve also the state-of-the-practice. Besides researchers and scientists of the field, the primary audience for the proposed book includes PhD students, academic teachers, as well as practitioners and professionals. Each of these categories of potential readers present adequate channels for marketing actions, e.g. scientific, academic or professional societies, internet-based communities, and authors or buyers of related publications.​

Applied Research in Uncertainty Modeling and Analysis

Download or Read eBook Applied Research in Uncertainty Modeling and Analysis PDF written by Bilal M. Ayyub and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 545 pages. Available in PDF, EPUB and Kindle.
Applied Research in Uncertainty Modeling and Analysis

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Publisher: Springer Science & Business Media

Total Pages: 545

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ISBN-10: 9780387235509

ISBN-13: 0387235507

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Book Synopsis Applied Research in Uncertainty Modeling and Analysis by : Bilal M. Ayyub

The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.

Uncertainty Modeling for Engineering Applications

Download or Read eBook Uncertainty Modeling for Engineering Applications PDF written by Flavio Canavero and published by Springer. This book was released on 2018-12-29 with total page 184 pages. Available in PDF, EPUB and Kindle.
Uncertainty Modeling for Engineering Applications

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Publisher: Springer

Total Pages: 184

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ISBN-10: 9783030048709

ISBN-13: 3030048705

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Book Synopsis Uncertainty Modeling for Engineering Applications by : Flavio Canavero

This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop “Uncertainty Modeling for Engineering Applications” (UMEMA 2017), held in Torino, Italy in November 2017.

Structural Design Optimization Considering Uncertainties

Download or Read eBook Structural Design Optimization Considering Uncertainties PDF written by Yannis Tsompanakis and published by CRC Press. This book was released on 2008-02-07 with total page 656 pages. Available in PDF, EPUB and Kindle.
Structural Design Optimization Considering Uncertainties

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Publisher: CRC Press

Total Pages: 656

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ISBN-10: 9780203938522

ISBN-13: 0203938526

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Book Synopsis Structural Design Optimization Considering Uncertainties by : Yannis Tsompanakis

Uncertainties play a dominant role in the design and optimization of structures and infrastructures. In optimum design of structural systems due to variations of the material, manufacturing variations, variations of the external loads and modelling uncertainty, the parameters of a structure, a structural system and its environment are not given, fi

Uncertainty Quantification and Predictive Computational Science

Download or Read eBook Uncertainty Quantification and Predictive Computational Science PDF 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.
Uncertainty Quantification and Predictive Computational Science

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Publisher: Springer

Total Pages: 345

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ISBN-10: 9783319995250

ISBN-13: 3319995251

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Book Synopsis Uncertainty Quantification and Predictive Computational Science by : Ryan G. McClarren

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.

Dynamics Under Uncertainty: Modeling Simulation and Complexity

Download or Read eBook Dynamics Under Uncertainty: Modeling Simulation and Complexity PDF written by Dragan Pamučar and published by . This book was released on 2021 with total page 210 pages. Available in PDF, EPUB and Kindle.
Dynamics Under Uncertainty: Modeling Simulation and Complexity

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Publisher:

Total Pages: 210

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ISBN-10: 3036515755

ISBN-13: 9783036515755

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Book Synopsis Dynamics Under Uncertainty: Modeling Simulation and Complexity by : Dragan Pamučar

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster-Shafer theory, etc.

Groundwater Detection Monitoring System Design under Conditions of Uncertainty

Download or Read eBook Groundwater Detection Monitoring System Design under Conditions of Uncertainty PDF written by Nuşin Buket Yenigül and published by Eburon Uitgeverij B.V.. This book was released on 2006 with total page 170 pages. Available in PDF, EPUB and Kindle.
Groundwater Detection Monitoring System Design under Conditions of Uncertainty

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Publisher: Eburon Uitgeverij B.V.

Total Pages: 170

Release:

ISBN-10: 9789059721425

ISBN-13: 905972142X

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Book Synopsis Groundwater Detection Monitoring System Design under Conditions of Uncertainty by : Nuşin Buket Yenigül