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- W2952677659 abstract "We present a dynamic model approximation strategy that allows to significantly increase computational efficiency of the simulation while maintaining proper validity. This can be used to effectively overcome the scalability constraints in state-of-the-art simulation frameworks for testing and validating large-scale systems. The method that we present leverages information theory metrics to measure the possible contribution of sub-areas in the simulation to the global behavior. This allows us to opportunistically approximate low-contributing areas and as a result decrease the computational cost of the simulation. We present a basic traffic-simulation use-case, implemented in the Acsim simulator to validate the proposed method and are able to achieve a 33% reduction of the computational cost. Furthermore, we analyze our proposed method from a more theoretical perspective." @default.
- W2952677659 created "2019-06-27" @default.
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- W2952677659 date "2019-04-01" @default.
- W2952677659 modified "2023-09-23" @default.
- W2952677659 title "Reducing Computational Cost Of Large-Scale Simulations Using Opportunistic Model Approximation" @default.
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- W2952677659 doi "https://doi.org/10.23919/springsim.2019.8732848" @default.
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