Explore the military and combat applications of modeling and simulation Engineering Principles of Combat Modeling and Distributed Simulation is the first book of its kind to address the three perspectives that simulation engineers must master for successful military and defense related modeling: the operational view (what needs to be modeled); the conceptual view (how to do combat modeling); and the technical view (how to conduct distributed simulation). Through methods from the fields of operations research, computer science, and engineering, readers are guided through the history, current training practices, and modern methodology related to combat modeling and distributed simulation systems. Comprised of contributions from leading international researchers and practitioners, this book provides a comprehensive overview of the engineering principles and state-of-the-art methods needed to address the many facets of combat modeling and distributed simulation and features the following four sections: Foundations introduces relevant topics and recommended practices, providing the needed basis for understanding the challenges associated with combat modeling and distributed simulation. Combat Modeling focuses on the challenges in human, social, cultural, and behavioral modeling such as the core processes of "move, shoot, look, and communicate" within a synthetic environment and also equips readers with the knowledge to fully understand the related concepts and limitations. Distributed Simulation introduces the main challenges of advanced distributed simulation, outlines the basics of validation and verification, and exhibits how these systems can support the operational environment of the warfighter. Advanced Topics highlights new and developing special topic areas, including mathematical applications fo combat modeling; combat modeling with high-level architecture and base object models; and virtual and interactive digital worlds. Featuring practical examples and applications relevant to industrial and government audiences, Engineering Principles of Combat Modeling and Distributed Simulation is an excellent resource for researchers and practitioners in the fields of operations research, military modeling, simulation, and computer science. Extensively classroom tested, the book is also ideal for courses on modeling and simulation; systems engineering; and combat modeling at the graduate level.

​This broad-ranging text/reference presents a fascinating review of the state of the art of modeling and simulation, highlighting both the seminal work of preeminent authorities and exciting developments from promising young researchers in the field. Celebrating the 50th anniversary of the Winter Simulation Conference (WSC), the premier international forum for disseminating recent advances in the field of system simulation, the book showcases the historical importance of this influential conference while also looking forward to a bright future for the simulation community. Topics and features: examines the challenge of constructing valid and efficient models, emphasizing the benefits of the process of simulation modeling; discusses model calibration, input model risk, and approaches to validating emergent behaviors in large-scale complex systems with non-linear interactions; reviews the evolution of simulation languages, and the history of the Time Warp algorithm; offers a focus on the design and analysis of simulation experiments under various goals, and describes how data can be “farmed” to support decision making; provides a comprehensive overview of Bayesian belief models for simulation-based decision making, and introduces a model for ranking and selection in cloud computing; highlights how input model uncertainty impacts simulation optimization, and proposes an approach to quantify and control the impact of input model risk; surveys the applications of simulation in semiconductor manufacturing, in social and behavioral modeling, and in military planning and training; presents data analysis on the publications from the Winter Simulation Conference, offering a big-data perspective on the significant impact of the conference. This informative and inspiring volume will appeal to all academics and professionals interested in computational and mathematical modeling and simulation, as well as to graduate students on the path to form the next generation of WSC pioneers.

This unique text/reference provides a comprehensive review of distributed simulation (DS) from the perspective of Model Driven Engineering (MDE), illustrating how MDE affects the overall lifecycle of the simulation development process. Numerous practical case studies are included to demonstrate the utility and applicability of the methodology, many of which are developed from tools available to download from the public domain. Topics and features: Provides a thorough introduction to the fundamental concepts, principles and processes of modeling and simulation, MDE and high-level architecture Describes a road map for building a DS system in accordance with the MDE perspective, and a technical framework for the development of conceptual models Presents a focus on federate (simulation environment) architectures, detailing a practical approach to the design of federations (i.e., simulation member design) Discusses the main activities related to scenario management in DS, and explores the process of MDE-based implementation, integration and testing Reviews approaches to simulation evolution and modernization, including architecture-driven modernization for simulation modernization Examines the potential synergies between the agent, DS, and MDE methodologies, suggesting avenues for future research at the intersection of these three fields Distributed Simulation – A Model Driven Engineering Approach is an important resource for all researchers and practitioners involved in modeling and simulation, and software engineering, who may be interested in adopting MDE principles when developing complex DS systems.

The capability modeling and simulation (M&S) supplies for managing systems complexity and investigating systems behaviors has made it a central activity in the development of new and existing systems. However, a handbook that provides established M&S practices has not been available. Until now. Modeling and Simulation-Based Systems Engineering Handbook details the M&S practices for supporting systems engineering in diverse domains. It discusses how you can identify systems engineering needs and adapt these practices to suit specific application domains, thus avoiding redefining practices from scratch. Although M&S practices are used and embedded within individual disciplines, they are often developed in isolation. However, they address recurring problems common to all disciplines. The editors of this book tackled the challenge by recruiting key representatives from several communities, harmonizing the different perspectives derived from individual backgrounds, and lining them up with the book’s vision. The result is a collection of M&S systems engineering examples that offer an initial means for cross-domain capitalization of the knowledge, methodologies, and technologies developed in several communities. These examples provide the pros and cons of the methods and techniques available, lessons learned, and pitfalls to avoid. As our society moves further in the information era, knowledge and M&S capabilities become key enablers for the engineering of complex systems and systems of systems. Therefore, knowledge and M&S methodologies and technologies become valuable output in an engineering activity, and their cross-domain capitalization is key to further advance the future practices in systems engineering. This book collates information across disciplines to provide you with the tools to more efficiently design and manage complex systems that achieve their goals.

An assessment of combat modeling & simulation technologies. Discusses simulators which allow military forces to practice & train in situations which would be too costly or risky to practice with real weapons. Chapters include: preparing for the next war; recent developments; simulators; types of simulations; the evolution of distributed interactive simulation; SIMNET; fidelity, fog & friction; verification, validation, & accreditation; standards for DIS scalability & scaling; funding for DIS systems. Graphs, charts, & photos.

A modern guide to computational models and constructive simulation for personalized patient care using the Digital Patient The healthcare industry’s emphasis is shifting from merely reacting to disease to preventing disease and promoting wellness. Addressing one of the more hopeful Big Data undertakings, The Digital Patient: Advancing Healthcare, Research, and Education presents a timely resource on the construction and deployment of the Digital Patient and its effects on healthcare, research, and education. The Digital Patient will not be constructed based solely on new information from all the “omics” fields; it also includes systems analysis, Big Data, and the various efforts to model the human physiome and represent it virtually. The Digital Patient will be realized through the purposeful collaboration of patients as well as scientific, clinical, and policy researchers. The Digital Patient: Advancing Healthcare, Research, and Education addresses the international research efforts that are leading to the development of the Digital Patient, the wealth of ongoing research in systems biology and multiscale simulation, and the imminent applications within the domain of personalized healthcare. Chapter coverage includes: The visible human The physiological human The virtual human Research in systems biology Multi-scale modeling Personalized medicine Self-quantification Visualization Computational modeling Interdisciplinary collaboration The Digital Patient: Advancing Healthcare, Research, and Education is a useful reference for simulation professionals such as clinicians, medical directors, managers, simulation technologists, faculty members, and educators involved in research and development in the life sciences, physical sciences, and engineering. The book is also an ideal supplement for graduate-level courses related to human modeling, simulation, and visualization.

Model Engineering for Simulation provides a systematic introduction to the implementation of generic, normalized and quantifiable modeling and simulation using DEVS formalism. It describes key technologies relating to model lifecycle management, including model description languages, complexity analysis, model management, service-oriented model composition, quantitative measurement of model credibility, and model validation and verification. The book clearly demonstrates how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. Guides systems and control engineers in the practical creation and delivery of simulation models using DEVS formalism Provides practical methods to improve credibility of models and manage the model lifecycle Helps readers gain an overall understanding of model lifecycle management and analysis Supported by an online ancillary package that includes an instructors and student solutions manual

This comprehensive text presents cutting-edge advances in the theory and methodology of modeling and simulation (M&S) and reveals how this work has been influenced by the fundamental contributions of Prof. Tuncer Ören to this field. Exploring the synergies among the domains of M&S and systems engineering (SE), the book describes how M&S and SE can help to address the complex problems identified as “Grand Challenges” more effectively under a model-driven and simulation-directed systems engineering framework. Features: examines frameworks for the development of advanced simulation methodologies; presents a focus on advanced modeling methodologies; reviews the reliability and quality assurance of models; discusses the specification and simulation of human and social behavior, including models of personality, emotions, conflict management, perception and anticipation; provides a survey of the body of knowledge in M&S; highlights the foundations established by the pioneering work of Prof. Tuncer Ören.

Commissioned by the Society for Modeling and Simulation International (SCS), this needed, useful new ‘Body of Knowledge’ (BoK) collects and organizes the common understanding of a wide collection of professionals and professional associations. Modeling and simulation (M&S) is a ubiquitous discipline that lays the computational foundation for real and virtual experimentation, clearly stating boundaries—and interactions—of systems, data, and representations. The field is well known, too, for its training support via simulations and simulators. Indeed, with computers increasingly influencing the activities of today’s world, M&S is the third pillar of scientific understanding, taking its place along with theory building and empirical observation. This valuable new handbook provides intellectual support for all disciplines in analysis, design and optimization. It contributes increasingly to the growing number of computational disciplines, addressing the broad variety of contributing as well as supported disciplines and application domains. Further, each of its sections provide numerous references for further information. Highly comprehensive, the BoK represents many viewpoints and facets, captured under such topics as: Mathematical and Systems Theory Foundations Simulation Formalisms and Paradigms Synergies with Systems Engineering and Artificial Intelligence Multidisciplinary Challenges Ethics and Philosophy Historical Perspectives Examining theoretical as well as practical challenges, this unique volume addresses the many facets of M&S for scholars, students, and practitioners. As such, it affords readers from all science, engineering, and arts disciplines a comprehensive and concise representation of concepts, terms, and activities needed to explain the M&S discipline. Tuncer Ören is Professor Emeritus at the University of Ottawa. Bernard Zeigler is Professor Emeritus at the University of Arizona. Andreas Tolk is Chief Scientist at The MITRE Corporation. All three editors are long-time members and Fellows of the Society for Modeling and Simulation International. Under the leadership of three SCS Fellows, Dr. Ören, University of Ottawa, Dr. Zeigler, The University of Arizona, and Dr. Tolk, The MITRE Corporation, more than 50 international scholars from 15 countries provided insights and experience to compile this initial M&S Body of Knowledge.

In this book, internationally recognized experts in philosophy of science, computer science, and modeling and simulation are contributing to the discussion on how ontology, epistemology, and teleology will contribute to enable the next generation of intelligent modeling and simulation applications. It is well understood that a simulation can provide the technical means to display the behavior of a system over time, including following observed trends to predict future possible states, but how reliable and trustworthy are such predictions? The questions about what we can know (ontology), how we gain new knowledge (epistemology), and what we do with this knowledge (teleology) are therefore illuminated from these very different perspectives, as each experts uses a different facet to look at these challenges. The result of bringing these perspectives into one book is a challenging compendium that gives room for a spectrum of challenges: from general philosophy questions, such as can we use modeling and simulation and other computational means at all to discover new knowledge, down to computational methods to improve semantic interoperability between systems or methods addressing how to apply the recent insights of service oriented approaches to support distributed artificial intelligence. As such, this book has been compiled as an entry point to new domains for students, scholars, and practitioners and to raise the curiosity in them to learn more to fully address the topics of ontology, epistemology, and teleology from philosophical, computational, and conceptual viewpoints.