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The topic of dynamic models tends to be splintered across various
disciplines, making it difficult to uniformly study the subject.
Moreover, the models have a variety of representations, from
traditional mathematical notations to diagrammatic and immersive
depictions. Collecting all of these expressions of dynamic models,
the Handbook of Dynamic System Modeling explores a panoply of
different types of modeling methods available for dynamical
systems. Featuring an interdisciplinary, balanced approach, the
handbook focuses on both generalized dynamic knowledge and specific
models. It first introduces the general concepts, representations,
and philosophy of dynamic models, followed by a section on modeling
methodologies that explains how to portray designed models on a
computer. After addressing scale, heterogeneity, and composition
issues, the book covers specific model types that are often
characterized by specific visual- or text-based grammars. It
concludes with case studies that employ two well-known commercial
packages to construct, simulate, and analyze dynamic models. A
complete guide to the fundamentals, types, and applications of
dynamic models, this handbook shows how systems function and are
represented over time and space and illustrates how to select a
particular model based on a specific area of interest.
Recently there has been considerable interest in qualitative
methods in simulation and mathematical model- ing. Qualitative
Simulation Modeling and Analysis is the first book to thoroughly
review fundamental concepts in the field of qualitative simulation.
The book will appeal to readers in a variety of disciplines
including researchers in simulation methodology, artificial
intelligence and engineering. This book boldly attempts to bring
together, for the first time, the qualitative techniques previously
found only in hard-to-find journals dedicated to single
disciplines. The book is written for scientists and engineers
interested in improving their knowledge of simulation modeling. The
"qualitative" nature of the book stresses concepts of invariance,
uncertainty and graph-theoretic bases for modeling and analysis.
Knowledge-Based Simulation: Methodology and Application represents
a recent compilation of research material that reviews fundamental
concepts of simulation methodology and knowledge-based simulation
applications. Knowledge-based simulation represents a new and
exciting bridge area linking the fields of computer simulation and
artificial intelligence. This book will appeal to both theorists
and practitioners who require simulation to solve complex problems.
A primary attraction of the book is its emphasis on both
methodology and applications. In this way, the reader can explore
new methods for encoding knowledge-inten- sive information into a
simulation model, and new applications that utilize these methods.
The topic of dynamic models tends to be splintered across various
disciplines, making it difficult to uniformly study the subject.
Moreover, the models have a variety of representations, from
traditional mathematical notations to diagrammatic and immersive
depictions. Collecting all of these expressions of dynamic models,
the Handbook of Dynamic System Modeling explores a panoply of
different types of modeling methods available for dynamical
systems. Featuring an interdisciplinary, balanced approach, the
handbook focuses on both generalized dynamic knowledge and specific
models. It first introduces the general concepts, representations,
and philosophy of dynamic models, followed by a section on modeling
methodologies that explains how to portray designed models on a
computer. After addressing scale, heterogeneity, and composition
issues, the book covers specific model types that are often
characterized by specific visual- or text-based grammars. It
concludes with case studies that employ two well-known commercial
packages to construct, simulate, and analyze dynamic models. A
complete guide to the fundamentals, types, and applications of
dynamic models, this handbook shows how systems function and are
represented over time and space and illustrates how to select a
particular model based on a specific area of interest.
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