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Dynamic simulation metamodeling using MARS: A case of radar simulation

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  • Bozağaç, Doruk
  • Batmaz, İnci
  • Oğuztüzün, Halit

Abstract

Dynamic system simulations require relating the inputs to the multivariate output which can be a function of time–space coordinates. In this work, we propose a methodology for the metamodeling of dynamic simulation models via Multivariate Adaptive Regression Splines (MARS). To handle incomplete output processes, where the simulation model does not produce an output in some steps due to missing inputs, we have devised a two-stage metamodeling scheme. The methodology is demonstrated on a dynamic radar simulation model. The prediction performance of the resulting metamodel is tested with four different sampling techniques (i.e., designs) and 16 sample sizes. We also investigate the effect of alternative coordinate system representations on the metamodeling performance. The results suggest that MARS is an effective method for metamodeling dynamic simulations, particularly, when expert judgment is not readily available. Results also show that there are interactions between the coordinate systems and sampling techniques, and some design-representation-size combinations are very promising in the metamodeling of radar simulations.

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  • Bozağaç, Doruk & Batmaz, İnci & Oğuztüzün, Halit, 2016. "Dynamic simulation metamodeling using MARS: A case of radar simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 124(C), pages 69-86.
    • Handle: RePEc:eee:matcom:v:124:y:2016:i:c:p:69-86
    DOI: 10.1016/j.matcom.2016.01.005
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    2. Mert Edali & Gönenç Yücel, 2020. "Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(6), pages 936-958, November.

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