Simulation and Model-Based Methodologies: An Integrative View

Simulation and Model-Based Methodologies: An Integrative View

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Description

Simulation, like a gem, is multi-faceted. Several subfields of simulation have emerged based on the characteristics of models used in a simulation study, on the nature and the generation characteristics of model behavior, also on the agent soch as a computer which generates model behavior. For example, one distinguishes: - deterministic simulation, stochastic simulation, stiff simulation based on functional rela- tionships of descriptive vuriables of models used; - combined simulation, continuous simulation, discrete simulation, process simulation, dis- crete event simulation, activity-scanning simulation based on characteristics of descrip- tive variables of modelS; variable topology simulation soch as moving boundary simulation, cellular simulation and fixed topology simulation soch as boundary-value simulation and network simulation (network flow simulation, Petri-net simulation, bond-graph simulation) based on spatial distribution of models; - simulation with fixed organization models (soch as simulation with hierarchical models) and simulation with variable organization models (i.e., autopoietic simulation) soch as metamorphic simulation, simulation with self-organizating models, simulation with self- learning models, evolutionary simulation based on orgCl"lization of component models; - state-maintaining simulation, behaviorally adaptive simulation, goal-seeking simulation, purposive simulation, purposeful simulation, ideal-seeking simulation based on goal(s) to be pursued by the model; - trajectory simulation, stroctural simulation, real-time simulation, predictive simulation, prescriptive simulation, intermittent simulation (such as regenerative simulation, opti- mizing simulation, gaming simulation, conferencing simulation, on-line simulation) based on nature md generation characteristics of model behavior; and - simulators such as aircraft simulator, earthquake simulator where physical analog can be
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Product details

  • Hardback | 668 pages
  • Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Berlin, Germany
  • English
  • biography
  • 3540128840
  • 9783540128847

Table of contents

Section 1: Conceptual Bases for System Modelling and Design.- 1: Model-Based Activities: A Paradigm Shift.- 2: System Paradigms as Reality Mappings.- 3: General Systems Framework for Inductive Modelling.- 4: System Theoretic Foundations of Modelling and Simulation.- 5: The Tricotyledon Theory of System Design.- 6: Concepts for Model-Based Policy Construction.- Section 2: Model-Based Simulation Architecture.- 7: Structures for Model-Based Simulation Systems.- 8: Symbolic Manipulation of System Models.- 9: Concepts for an Advanced Parallel Simulation Architecture.- Section 3: Impact of Formalisms on Model Specification.- 10: GEST-A Modelling and Simulation Language Based on System Theoretic Concepts.- 11: Continuous and Discontinuous-Change Models: Concepts for Simulation Languages.- 12: Discrete Event Formalism and Simulation Model Development.- Section 4: Model Identification, Reconstruction, and Optimization.- 13: Structure Characterization for I11-Defined Systems.- 14: Reconstructability Analysis: An Overview.- 15: SAPS-A Software System for Inductive Modelling.- 16: Optimization in Simulation Studies.- Section 5: Quality Assurance in Model-Based Activities.- 17: Quality Assurance in Modelling and Simulation: A Taxonomy.- 18: How to Enhance the Robustness of Simulation Software.- 19: Simulation Model Validation.- 20: Critical Issues in Evaluating Socio-Economic Models.- Section 6: Contributed Workshop Presentations.- Group 1: Model-Based Simulation Architecture.- Group 2: Impact of Formalisms on Model Specification.- Group 3: Model Identification, Reconstruction, and Optimization.- Group 4: Quality Assurance in Model-Based Activities.
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