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- W2079353459 abstract "Simulation modeling of complex systems is receiving increasing research attention over the past years. In this paper, we discuss the basic concepts involved in multi- resolution simulation modeling of complex stochastic systems. We argue that, in many cases, using the average over all available high-resolution simulation results as the input to subsequent low-resolution modules is inappropriate and may lead to erroneous final results. Instead high- resolution output data should be classified into groups that match underlying patterns or features of the system behavior before sensing group averages to the low-resolution modules. We propose high-dimensional data clustering as a key interfacing component between simulation modules with different resolutions and use unsupervised learning schemes to recover the patterns for the high-resolution simulation results. We give some examples to demonstrate our proposed scheme." @default.
- W2079353459 created "2016-06-24" @default.
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- W2079353459 date "2000-06-23" @default.
- W2079353459 modified "2023-09-23" @default.
- W2079353459 title "<title>Clustering methods for multiresolution simulation modeling</title>" @default.
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- W2079353459 doi "https://doi.org/10.1117/12.389385" @default.
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