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Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics #10)
by Reuven Y. Rubinstein Dirk P. KroeseThis accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as importance (re-)sampling, and the transform likelihood ratio method, the score function method for sensitivity analysis, the stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization, the cross-entropy method for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on: • Random number generation, including multiple-recursive generators and the Mersenne Twister • Simulation of Gaussian processes, Brownian motion, and diffusion processes • Multilevel Monte Carlo method • New enhancements of the cross-entropy (CE) method, including the “improved” CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters • Over 100 algorithms in modern pseudo code with flow control • Over 25 new exercises Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting. Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling.
Simulation and Visualization on the Grid: Parallelldatorcentrum Kungl Tekniska Högskolan Seventh Annual Conference Stockholm, Sweden December 1999 Proceedings (Lecture Notes in Computational Science and Engineering #13)
by Bjö Engquist Lennart Johnsson Michael Hammill Faith ShortIt is now 30 years since the network for digital communication, the ARPA-net, first came into operation. Since the first experiments with sending electronic mail and performing file transfers, the development of networks has been truly remarkable. Today's Internet continues to develop at an exponential rate that even surpasses that of computing and storage technologies. About five years after being commercialized, it has become as pervasive as the tele phone had become 30 years after its initial deployment. In the United States, the size of the Internet industry already exceeds that of the auto industry, which has been in existence for about 100 years. The exponentially increas ing capabilities of communication, computing, and storage systems is also reshaping the way science and engineering are pursued. Large-scale simulation studies in chemistry, physics, engineering, and sev eral other disciplines may now produce data sets of ,several terabytes or petabytes. Similarly, almost all measurements today produce data in digital form, whether from collections of sensors, three-dimensional digital images, or video. These data sets often represent complex phenomena that require rich visualization capabilities and efficient data-mining techniques to under stand. Furthermore, the data may be produced and archived in several differ ent locations, and the analysis carried out by teams with members at several locations-possibly distinct from those with significant storage, computation, or visualization facilities. The emerging computational Grids enable the transparent use of remote instruments, computational and data resources.
Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
by Hyeong Soo Chang Michael C. Fu Jiaqiao Hu Steven I. MarcusMarkov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.
Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)
by Hyeong Soo Chang Jiaqiao Hu Michael C. Fu Steven I. MarcusMarkov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.
Simulation-Based Analysis of Energy and Carbon Emissions in the Housing Sector: A System Dynamics Approach (Green Energy and Technology)
by Michael Gbolagade Oladokun Clinton Ohis AigbavboaThis book describes the development of a system dynamics-based model that can capture the future trajectories of housing energy and carbon emissions. It approaches energy and carbon emissions in the housing sector as a complex socio-technical problem involving the analysis of intrinsic interrelationships among dwellings, occupants and the environment. Based on an examination of the UK housing sector but with relevance worldwide, the book demonstrates how the systems dynamics simulation can be used as a learning laboratory regarding future trends in housing energy and carbon emissions. The authors employ a pragmatic research strategy, involving the collection of both qualitative and quantitative data to develop a model. The book enriches readers’ understanding of the complexity involved in housing energy and carbon emissions from a systems-thinking perspective. As such, it will be of interest to researchers in the fields of architectural engineering, housing studies and climate change, while also appealing to industry practitioners and policymakers specializing in housing energy.
Simulation-Based Mechanical Design (Synthesis Lectures on Mechanical Engineering)
by Xiaobin LeThis book establishes a modern practical approach to mechanical design. It introduces a full set of mechanical design theories and approaches to conduct and complete mechanical design tasks. The book uses Finite-Element Analysis (FEA) as a mechanical engineering tool to calculate stress/strain and then integrate it with failure theory to complete the mechanical design. FEA simulation always evaluates the stress and strain of any component/assembly no matter whether components/assemblies have complicated geometries and/or are under complicated loading conditions.
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series #25)
by Abhijit GosaviSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to convergence analysis of some of the methods enumerated above. *Computer programs for many algorithms of simulation-based optimization.
Simulation-Driven Design by Knowledge-Based Response Correction Techniques
by Slawomir Koziel Leifur LeifssonFocused on efficient simulation-driven multi-fidelity optimization techniques, this monograph on simulation-driven optimization covers simulations utilizing physics-based low-fidelity models, often based on coarse-discretization simulations or other types of simplified physics representations, such as analytical models. The methods presented in the book exploit as much as possible any knowledge about the system or device of interest embedded in the low-fidelity model with the purpose of reducing the computational overhead of the design process. Most of the techniques described in the book are of response correction type and can be split into parametric (usually based on analytical formulas) and non-parametric, i.e., not based on analytical formulas. The latter, while more complex in implementation, tend to be more efficient.The book presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included. The book demonstrates the use of the discussed techniques for solving real-world engineering design problems, including applications in microwave engineering, antenna design, and aero/hydrodynamics.
Simulation-Driven Modeling and Optimization: ASDOM, Reykjavik, August 2014 (Springer Proceedings in Mathematics & Statistics #153)
by Slawomir Koziel Leifur Leifsson Xin-She YangThis edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth along with an array of the latest techniques to optimize the efficiency of the simulation-driven design process.High-fidelity simulation models allow for accurate evaluations of the devices and systems, which is critical in the design process, especially to avoid costly prototyping stages. Despite this and other advantages, the use of simulation tools in the design process is quite challenging due to associated high computational cost. The steady increase of available computational resources does not always translate into the shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. For this reason, automated simulation-driven design—while highly desirable—is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive.
Simulation for a Sustainable Future: 11th Congress, EUROSIM 2023, Amsterdam, The Netherlands, July 3–5, 2023, Proceedings, Part I (Communications in Computer and Information Science #2032)
by Miguel Mujica Mota Paolo ScalaThe two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3–5, 2023. The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.
Simulation for Applied Graph Theory Using Visual C++
by Shaharuddin Salleh Zuraida Abal AbasThe tool for visualization is Microsoft Visual C++. This popular software has the standard C++ combined with the Microsoft Foundation Classes (MFC) libraries for Windows visualization. This book explains how to create a graph interactively, solve problems in graph theory with minimum number of C++ codes, and provide friendly interfaces that makes learning the topics an interesting one. Each topic in the book comes with working Visual C++ codes which can easily be adapted as solutions to various problems in science and engineering.
Simulation for Policy Inquiry
by Anand DesaiPublic policy and management problems have been described as poorly defined, messy, squishy, unstructured, intractable, and wicked. In a word, they are complex. This book illustrates the development and use of simulation models designed to capture some of the complexity inherent in the formulation, management, and implementation of policies aimed at addressing such problems. Simulation models have long existed at the fringes of policy inquiry but are not yet considered an essential component of the policy analyst’s toolkit. However, this situation is likely to change because with improvements in computational power and software, simulation is now easier to include in the standard repertoire of research tools available for discovery and decision support. This volume provides both a conceptual rationale for using simulations to inform public policy and a practical introduction to how such models might be constructed and employed. The focus of these papers is on the uses of simulation to gain understanding and inform policy decisions and action. Techniques represented in this volume include Monte Carlo simulation, system dynamics and agent based modeling.
Simulation in der Regelungstechnik (Fachberichte Simulation #12)
by Karl H. Fasol Klaus DiekmannDie Simulation wird in der Regelungstechnik zum Entwurf von Regelkreisen, zur Prozeßüberwachung sowie zur regelungstechnischen Ausbildung genutzt. Anhand von Simulationen können theoretisch oder experimentell ermittelte Modelle entworfen und getestet werden; das gesamte Regelkreisverhalten kann optimiert werden. Die verschiedenen Simulationstechniken werden gegenüber gestellt, so daß jeweils die günstigste Wahl bezüglich des einzusetzenden Simulationswerkzeugs bzw. -verfahrens getroffen werden kann.
The Simulation Metamodel
by Linda Weiser FriedmanResearchers develop simulation models that emulate real-world situations. While these simulation models are simpler than the real situation, they are still quite complex and time consuming to develop. It is at this point that metamodeling can be used to help build a simulation study based on a complex model. A metamodel is a simpler, analytical model, auxiliary to the simulation model, which is used to better understand the more complex model, to test hypotheses about it, and provide a framework for improving the simulation study. The use of metamodels allows the researcher to work with a set of mathematical functions and analytical techniques to test simulations without the costly running and re-running of complex computer programs. In addition, metamodels have other advantages, and as a result they are being used in a variety of ways: model simplification, optimization, model interpretation, generalization to other models of similar systems, efficient sensitivity analysis, and the use of the metamodel's mathematical functions to answer questions about different variables within a simulation study.
Simulation mit dem Warteschlangensimulator: Mathematische Modellierung und Simulation von Produktions- und Logistikprozessen (Studienbücher Wirtschaftsmathematik)
by Alexander HerzogDieses Buch verknüpft die mathematischen Grundlagen der Warteschlangentheorie mit der Modellierung praktischer Problemstellungen, der Anwendung entsprechender Simulationen und der validen Auswertung ihrer Ergebnisse. In zahlreichen konkreten Beispielen und Fragestellungen kommt der frei verfügbare Warteschlangensimulator zum Einsatz, so dass alles nachvollzogen und für eigene Zwecke adaptiert werden kann. Das Buch bildet somit eine solide Basis für den erfolgreichen Einsatz von Warteschlangensimulationen, etwa zur Planung von Produktions- und Logistikprozessen: Mathematiker erhalten hier neue Impulse und Anwender aus der industriellen Praxis einen gut zugänglichen Einstieg in das Thema.
Simulation Modeling and Arena (Wiley Series In Modeling And Simulation Ser.)
by Manuel D. RossettiEmphasizes a hands-on approach to learning statistical analysis and model building through the use of comprehensive examples, problems sets, and software applications With a unique blend of theory and applications, Simulation Modeling and Arena®, Second Edition integrates coverage of statistical analysis and model building to emphasize the importance of both topics in simulation. Featuring introductory coverage on how simulation works and why it matters, the Second Edition expands coverage on static simulation and the applications of spreadsheets to perform simulation. The new edition also introduces the use of the open source statistical package, R, for both performing statistical testing and fitting distributions. In addition, the models are presented in a clear and precise pseudo-code form, which aids in understanding and model communication. Simulation Modeling and Arena, Second Edition also features: Updated coverage of necessary statistical modeling concepts such as confidence interval construction, hypothesis testing, and parameter estimation Additional examples of the simulation clock within discrete event simulation modeling involving the mechanics of time advancement by hand simulation A guide to the Arena Run Controller, which features a debugging scenario New homework problems that cover a wider range of engineering applications in transportation, logistics, healthcare, and computer science A related website with an Instructor’s Solutions Manual, PowerPoint® slides, test bank questions, and data sets for each chapter Simulation Modeling and Arena, Second Edition is an ideal textbook for upper-undergraduate and graduate courses in modeling and simulation within statistics, mathematics, industrial and civil engineering, construction management, business, computer science, and other departments where simulation is practiced. The book is also an excellent reference for professionals interested in mathematical modeling, simulation, and Arena.
Simulation Modeling and Arena
by Manuel D. RossettiEmphasizes a hands-on approach to learning statistical analysis and model building through the use of comprehensive examples, problems sets, and software applications With a unique blend of theory and applications, Simulation Modeling and Arena®, Second Edition integrates coverage of statistical analysis and model building to emphasize the importance of both topics in simulation. Featuring introductory coverage on how simulation works and why it matters, the Second Edition expands coverage on static simulation and the applications of spreadsheets to perform simulation. The new edition also introduces the use of the open source statistical package, R, for both performing statistical testing and fitting distributions. In addition, the models are presented in a clear and precise pseudo-code form, which aids in understanding and model communication. Simulation Modeling and Arena, Second Edition also features: Updated coverage of necessary statistical modeling concepts such as confidence interval construction, hypothesis testing, and parameter estimation Additional examples of the simulation clock within discrete event simulation modeling involving the mechanics of time advancement by hand simulation A guide to the Arena Run Controller, which features a debugging scenario New homework problems that cover a wider range of engineering applications in transportation, logistics, healthcare, and computer science A related website with an Instructor’s Solutions Manual, PowerPoint® slides, test bank questions, and data sets for each chapter Simulation Modeling and Arena, Second Edition is an ideal textbook for upper-undergraduate and graduate courses in modeling and simulation within statistics, mathematics, industrial and civil engineering, construction management, business, computer science, and other departments where simulation is practiced. The book is also an excellent reference for professionals interested in mathematical modeling, simulation, and Arena.
Simulation Modeling for Watershed Management
by James WesterveltA discussion of the role of modeling in the management process, with an overview of state-of-the-art modeling applications. The first chapters provide a background on the benefits and costs of modeling and on the ecological basis of models, using historical applications as examples, while the second section describes the latest models from a wide selection of environmental disciplines. Since management frequently requires the integration of knowledge from many different areas, both single discipline and multidiscipline models are discussed in detail, and the author emphasizes the importance of understanding the issues and alternatives in choosing, applying, and evaluating models. Land and watershed managers as well as students of forestry, park management, regional planing and agriculture will find this a thorough and practical introduction to all aspects of modeling.
Simulation of Additive Manufacturing using Meshfree Methods: With Focus on Requirements for an Accurate Solution (Lecture Notes in Applied and Computational Mechanics #97)
by Christian WeißenfelsThis book provides a detailed instruction to virtually reproduce the processes of Additive Manufacturing on a computer. First, all mathematical equations needed to model these processes are presented. Due to their flexibility, meshfree methods represent optimal computational solution schemes to simulate Additive Manufacturing processes. On the other hand, these methods usually do not guarantee an accurate solution. For this reason, this monograph is dedicated in detail to the necessary criteria for computational solution schemes to provide accurate results. Several meshfree methods are examined with respect to these conditions. Two different 3D printing techniques are presented in detail. The results obtained from the simulation are investigated and compared with experimental data. This work is addressed to both scientists and professionals working in the field of development who are interested to learn the secrets behind meshfree methods or get into the modeling of Additive Manufacturing.
Simulation of Dynamic Systems with MATLAB and Simulink
by Harold KleeSimulation is increasingly important for students in a wide variety of fields, from engineering and physical sciences to medicine, biology, economics, and applied mathematics. Current trends point toward interdisciplinary courses in simulation intended for all students regardless of their major, but most textbooks are subject-specific and consequen
Simulation of Dynamic Systems with MATLAB and Simulink
by Harold Klee Randal Allen"� a seminal text covering the simulation design and analysis of a broad variety of systems using two of the most modern software packages available today. � particularly adept [at] enabling students new to the field to gain a thorough understanding of the basics of continuous simulation in a single semester, and [also provides] a more advanced tre
Simulation of Dynamic Systems with MATLAB and Simulink
by Harold Klee Randal Allen"� a seminal text covering the simulation design and analysis of a broad variety of systems using two of the most modern software packages available today. � particularly adept [at] enabling students new to the field to gain a thorough understanding of the basics of continuous simulation in a single semester, and [also provides] a more advanced tre
Simulation of Dynamic Systems with MATLAB® and Simulink®
by Harold Klee Randal AllenContinuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems. The book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. It features updated chapters and two new sections on Black Swan and the Stochastic Information Packet (SIP) and Stochastic Library Units with Relationships Preserved (SLURP) Standard. The new edition includes basic concepts, mathematical tools, and the common principles of various simulation models for different phenomena, as well as an abundance of case studies, real-world examples, homework problems, and equations to develop a practical understanding of concepts.
Simulation of Dynamic Systems with MATLAB and Simulink, Third Edition
by Harold Klee Randal AllenThe book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. This third edition features new material on Stochastic Information Packets (SIPs), Stochastic Library Units with Relationships Preserved (SLURPs), and Black Swans. It explains the process of converting a mathematical model of a continuous or discrete system to a simulation model and source code implementation, which can be explored to better understand the dynamic behavior of the system. For reinforced learning, the text features MATLAB and Simulink extensively, as well as many illustrative homework problems.
Simulation of ODE/PDE Models with MATLAB®, OCTAVE and SCILAB: Scientific and Engineering Applications
by Alain Vande Wouwer Philippe Saucez Carlos VilasSimulation of ODE/PDE Models with MATLAB®, OCTAVE and SCILAB shows the reader how to exploit a fuller array of numerical methods for the analysis of complex scientific and engineering systems than is conventionally employed. The book is dedicated to numerical simulation of distributed parameter systems described by mixed systems of algebraic equations, ordinary differential equations (ODEs) and partial differential equations (PDEs). Special attention is paid to the numerical method of lines (MOL), a popular approach to the solution of time-dependent PDEs, which proceeds in two basic steps: spatial discretization and time integration.Besides conventional finite-difference and element techniques, more advanced spatial-approximation methods are examined in some detail, including nonoscillatory schemes and adaptive-grid approaches. A MOL toolbox has been developed within MATLAB®/OCTAVE/SCILAB. In addition to a set of spatial approximations and time integrators, this toolbox includes a collection of application examples, in specific areas, which can serve as templates for developing new programs.Simulation of ODE/PDE Models with MATLAB®, OCTAVE and SCILAB provides a practical introduction to some advanced computational techniques for dynamic system simulation, supported by many worked examples in the text, and a collection of codes available for download from the book’s page at www.springer.com. This text is suitable for self-study by practicing scientists and engineers and as a final-year undergraduate course or at the graduate level.