Assessing candidates for model abstraction

Richard A. MacDonald; Robert M. McGraw

doi:10.1117/12.389384

23 June 2000 Assessing candidates for model abstraction

Richard A. MacDonald, Robert M. McGraw

Proceedings Volume 4026, Enabling Technology for Simulation Science IV; (2000) https://doi.org/10.1117/12.389384
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Today's modeling and simulation community is faced with the problem of developing and managing large complex system models comprised of a diverse set of subsystem component models. These component models may be described using varying amounts of detail and fidelity as well as differing modeling paradigms. Often, a complex simulation comprised of high fidelity subcomponent models may result in a more detailed system model than the simulation objective requires. Simulating such a system model results in a waste of simulation time with respect to addressing the simulation goals. One way to avoid wasting simulation cycles is to reduce the complexity of subcomponent models while not affecting the desired simulation objective. The process of reducing the complexity of these subcomponent models is known as abstract modeling. Abstract modeling reduces the subcomponent model complexity by eliminating, grouping, or estimating model parameters or variables at a less detailed level without grossly affecting the simulation results. One key issue concerning model abstracting is identifying the variables or parameters that can be abstracted away for a given simulation objective. This paper presents an approach to identifying candidate variables for model abstraction when considering typical C4ISR (Command, Control, Computers, Communications, Intelligence, Surveillance, and Reconnaissance) hardware systems.

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Richard A. MacDonald and Robert M. McGraw "Assessing candidates for model abstraction", Proc. SPIE 4026, Enabling Technology for Simulation Science IV, (23 June 2000); https://doi.org/10.1117/12.389384

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KEYWORDS

Systems modeling

Performance modeling

C4ISR

Data modeling

Electromagnetic simulation

Statistical modeling

Computer simulations

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