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20 June 1997 Abstraction methodology based on parameter morphism
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Complex interactions of natural and artificial processes over both time and space calls for powerful new modeling methodologies. Approaches based on partial differential equations entail an enormous computational burden that greatly limits their applicability. Deriving DEVS representations with a well justified process of abstraction from traditional differential equation models can assure relative validity and realism while gaining orders of magnitude speedup. However, when executed within an optimization loop, distributed models must still be greatly simplified in order to allow with-our- lifetime convergence. To perform such simplification, we propose a multiresolution search strategy utilizing aggregation through parameter morphisms. The idea is that we can successively improve the result of optimization by successively increasing model resolution which narrowing its scope through constraint propagation of parameter values from low resolution to successively higher resolution models.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yoonkeon Moon and Bernard P. Zeigler "Abstraction methodology based on parameter morphism", Proc. SPIE 3083, Enabling Technology for Simulation Science, (20 June 1997);

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