Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs. It is widely applied in engineering, in business, and in the physical and social sciences. Simulation method ology araws on computer. science, statistics, and operations research and is now sufficiently developed and coherent to be called a discipline in its own right. A course in simulation is an essential part of any operations re search or computer science program. A large fraction of applied work in these fields involves simulation; the techniques of simulation, as tools, are as fundamental as those of linear programming or compiler construction, for example. Simulation sometimes appears deceptively easy, but perusal of this book will reveal unexpected depths. Many simulation studies are statistically defective and many simulation programs are inefficient. We hope that our book will help to remedy this situation. It is intended to teach how to simulate effectively. A simulation project has three crucial components, each of which must always be tackled: (1) data gathering, model building, and validation; (2) statistical design and estimation; (3) programming and implementation. Generation of random numbers (Chapters 5 and 6) pervades simulation, but unlike the three components above, random number generators need not be constructed from scratch for each project. Usually random number packages are available. That is one reason why the chapters on random numbers, which contain mainly reference material, follow the ch!lPters deal ing with experimental design and output analysis.
Molecular dynamics simulations have become instrumental in replacing our view of proteins as relatively rigid structures with the realization that they were dynamic systems, whose internal motions play a functional role. Over the years, such simulations have become a central part of biophysics. Applications of molecular dynamics in biophysics range over many areas. They are used in the structure determination of macromolecules with x-ray and NMR data, the modelling of unknown structures from their sequence, the study of enzyme mechanisms, the estimation of ligand-binding free energies, the evaluation of the role of conformational change in protein function, and drug design for targets of known structures. The widespread application of molecular dynamics and related methodologies suggests that it would be useful to have available an introductory self-contained course by which students with a relatively limited background in chemistry, biology and computer literacy, can learn the fundamentals of the field. This Guide to Biomolecular Simulations tries to fill this need. The Guide consists of six chapters which provide the fundamentals of the field and six chapters which introduce the reader to more specialized but important applications of the methodology.
"Ready to blow your mind? Spend 15 seconds reading Clark Aldrich's The Complete Guide to Simulations and Serious Games. Witty, fast-paced, and non-linear -- it's Spock meets Alton Brown." -- Lynne Kenney, Psy.D., The Family Coach This exciting work offers designers a new way to see the world, model it, and present it through simulations. A groundbreaking resource, it includes a wealth of new tools and terms and a corresponding style guide to help understand them. The author -- a globally recognized industry guru -- covers topics such as virtual experiences, games, simulations, educational simulations, social impact games, practiceware, game-based learning/digital game based learning, immersive learning, and serious games. This book is the first of its kind to present definitions of more than 600 simulation and game terms, concepts, and constructs.
Changes and additions are sprinkled throughout. Among the significant new features are: • Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5); • gradient estimation (Sections 1. 6, 2. 5, and 4. 9); • better handling of asynchronous observations (Sections 3. 3 and 3. 6); • radically updated treatment of indirect estimation (Section 3. 3); • new section on standardized time series (Section 3. 8); • better way to generate random integers (Section 6. 7. 1) and fractions (Appendix L, program UNIFL); • thirty-seven new problems plus improvements of old problems. Helpful comments by Peter Glynn, Barry Nelson, Lee Schruben, and Pierre Trudeau stimulated several changes. Our new random integer routine extends ideas of Aarni Perko. Our new random fraction routine implements Pierre L'Ecuyer's recommended composite generator and provides seeds to produce disjoint streams. We thank Springer-Verlag and its late editor, Walter Kaufmann-Bilhler, for inviting us to update the book for its second edition. Working with them has been a pleasure. Denise St-Michel again contributed invaluable text-editing assistance. Preface to the First Edition Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs. It is widely applied in engineering, in business, and in the physical and social sciences.
"With the growing use of simulation within the field of healthcare, Healthcare Simulation: A Guide for Operations Specialists provides a much needed resource for developing the roles and responsibilities of simulation operations specialists. The book illustrates the current state and evolution of the simulation professional workforce and discusses the topics necessary for the development of this pivotal role. The book promotes the value of simulation in healthcare and its associated outcomes while clarifying the operational requirements of successful simulations. Featuring numerous contributions from international experts, consultants, and specialists, Healthcare Simulation: A Guide for Operations Specialists presents advances in healthcare simulation techniques and also features:"--Provided by publishe
This invaluable text/reference reviews the state of the art in simulation-based approaches across a wide range of different disciplines, and provides evidence of using simulation-based approaches to advance these disciplines. Highlighting the benefits that simulation can bring to any field, the volume presents case studies by the leading experts from such diverse domains as the life sciences, engineering, architecture, arts, and social sciences. Topics and features: includes review questions at the end of every chapter; provides a broad overview of the evolution of the concept of simulation, stressing its importance across numerous sectors and disciplines; addresses the role of simulation in engineering design, and emphasizes the benefits of integrating simulation into the systems engineering paradigm; explains the relation of simulation with Cyber-Physical Systems and the Internet of Things, and describes a simulation infrastructure for complex adaptive systems; investigates how simulation is used in the Software Design Life Cycle to assess complex solutions, and examines the use of simulation in architectural design; reviews the function and purpose of simulation within the context of the scientific method, and its contribution to healthcare and health education training; discusses the position of simulation in research in the social sciences, and describes the simulation of service systems for simulation-based enterprise management; describes the role of simulation in learning and education, as well as in in military training. With its near-exhaustive coverage of disciplines, this comprehensive collection is essential reading for all researchers, practitioners and students seeking insights into the use of various modeling paradigms and the need for robust simulation infrastructure to advance their field into a computational future.
Information storage and retrieval systems by Robert T. Milhous
This guide to the PSpice circuit simulator provides a tutorial approach to using PSpice through graduated examples. This edition includes enhanced pedagogical features, and coverage of the newest capabilities of this program. It explains: the use of Monte Carlo methods in PSpice for statistically computing estimates of how circuits will behave with variations in component values and derivation and use of two-port parameters, including s-parameters. It also includes an expanded section on group and time delay, and on noise analysis, as well as fuller descriptions and examples for using parameters, functions and values defined by formulas to generalize circuit blocks and specify component values.
From a review of the Second Edition 'If you are new to the field and want to know what "all this Verilog stuff is about," you've found the golden goose. The text here is straight forward, complete, and example rich -mega-multi-kudos to the author James Lee. Though not as detailed as the Verilog reference guides from Cadence, it likewise doesn't suffer from the excessive abstractness those make you wade through. This is a quick and easy read, and will serve as a desktop reference for as long as Verilog lives. Best testimonial: I'm buying my fourth and fifth copies tonight (I've loaned out/lost two of my others).' Zach Coombes, AMD
A guide to the use of PSpice in common electrical and electronic problems. This revised edition features two-port network analysis, loop gain analysis, and expanded coverage of group and time delay, noise analysis and macros. Software supplements are available for the IBM PC, IBM PS/2 and Mac 2.
This accessible text presents a detailed introduction to the use of a wide range of software tools and modeling environments for use in the biosciences, as well as the fundamental mathematical background. The practical constraints presented by each modeling technique are described in detail, enabling the researcher to determine which software package would be most useful for a particular problem. Features: introduces a basic array of techniques to formulate models of biological systems, and to solve them; discusses agent-based models, stochastic modeling techniques, differential equations, spatial simulations, and Gillespie’s stochastic simulation algorithm; provides exercises; describes such useful tools as the Maxima algebra system, the PRISM model checker, and the modeling environments Repast Simphony and Smoldyn; contains appendices on rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts; offers supplementary material at an associated website.
This book provides readers with a detailed orientation to healthcare simulation research, aiming to provide descriptive and illustrative accounts of healthcare simulation research (HSR). Written by leaders in the field, chapter discussions draw on the experiences of the editors and their international network of research colleagues. This seven-section practical guide begins with an introduction to the field by relaying the key components of HSR. Sections two, three, four, and five then cover various topics relating to research literature, methods for data integration, and qualitative and quantitative approaches. Finally, the book closes with discussions of professional practices in HSR, as well as helpful tips and case studies.Healthcare Simulation Research: A Practical Guide is an indispensable reference for scholars, medical professionals and anyone interested in undertaking HSR.
A unique guide to the design and implementation of simulationsoftware This book offers a concise introduction to the art of buildingsimulation software, collecting the most important concepts andalgorithms in one place. Written for both individuals new to thefield of modeling and simulation as well as experiencedpractitioners, this guide explains the design and implementation ofsimulation software used in the engineering of large systems whilepresenting the relevant mathematical elements, concept discussions,and code development. The book approaches the topic from the perspective of Zeigler'stheory of modeling and simulation, introducing the theory'sfundamental concepts and showing how to apply them to engineeringproblems. Readers will learn five necessary skills for buildingsimulations of complicated systems: Working with fundamental abstractions for simulating dynamicsystems Developing basic simulation algorithms for continuous anddiscrete event models Combining continuous and discrete event simulations into acoherent whole Applying strategies for testing a simulation Understanding the theoretical foundations of the modelingconstructs and simulation algorithms The central chapters of the book introduce, explain, anddemonstrate the elements of the theory that are most important forbuilding simulation tools. They are bracketed by applications torobotics, control and communications, and electric power systems;these comprehensive examples clearly illustrate how the conceptsand algorithms are put to use. Readers will explore the design ofobject-oriented simulation programs, simulation using multi-coreprocessors, and the integration of simulators into larger softwaresystems. The focus on software makes this book particularly useful forcomputer science and computer engineering courses in simulationthat focus on building simulators. It is indispensable reading forundergraduate and graduate students studying modeling andsimulation, as well as for practicing scientists and engineersinvolved in the development of simulation tools.
This easy-to-follow textbook provides an exercise-driven guide to the use of the Discrete Event Systems Specification (DEVS) simulation modeling formalism and the System Entity Structure (SES) simulation model ontology supported with the latest advances in software architecture and design principles, methods, and tools for building and testing virtual Systems of Systems (SoS). The book examines a wide variety of SoS problems, ranging from cloud computing systems to biological systems in agricultural food crops. This enhanced and expanded second edition also features a new chapter on DEVS support for Markov modeling and simulation. Topics and features: provides an extensive set of exercises throughout the text to reinforce the concepts and encourage use of the tools, supported by introduction and summary sections; discusses how the SoS concept and supporting virtual build and test environments can overcome the limitations of current approaches; offers a step-by-step introduction to the DEVS concepts and modeling environment features required to build sophisticated SoS models; describes the capabilities and use of the tools CoSMoS/DEVS-Suite, Virtual Laboratory Environment, and MS4 MeTM; reviews a range of diverse applications, from the development of new satellite design and launch technologies, to surveillance and control in animal epidemiology; examines software/hardware co-design for SoS, and activity concepts that bridge information-level requirements and energy consumption in the implementation; demonstrates how the DEVS formalism supports Markov modeling within an advanced modeling and simulation environment (NEW). This accessible and hands-on textbook/reference provides invaluable practical guidance for graduate students interested in simulation software development and cyber-systems engineering design, as well as for practitioners in these, and related areas.