Editorial message: special track on applications of spatial simulation of discrete entities

ABSTRACT

Many important phenomena result from localized interactions including: population dynamics and epidemics, cell and tissue modeling, mobile computing and wireless networks. Modeling these systems can reduce experimentation costs or enable non-destructive in silica experimentation.Simulation models can be classified depending on how they represent reality, in particular time, space and simulated entities (objects). The simplest to described are models based on ordinary differential equations, which are aspatial and they may have either single time step for all objects, making time synchronous, or different time steps for different entities, making time asynchronous. Simulation entities are also described by a set of read valued parameters, so they are treated as continuous. It is well known that spatially explicit models can exhibit qualitatively different results than their aspatial counterparts [5]. This makes the models based on partial differential equations very common and popular. In this track however, we focus on a different category of models. To make this difference clear, we start with the following categorization of all models based on how they treat the three orthogonal aspects of simulations: simulation objects (entities) and the world that they inhabit (time and space). The categorization is shown in Table 1.The field of numerical computing is well studied, with numerous forums for extensive work and publication of spatial models using continuous time and treating entities as continuous (e.g. diffusion models [3, 1] or finite element method approaches [6, 11]. Traditionally, techniques for spatial modeling of discrete systems have tended to be published in application specific forums. In forming this track, we hoped to provide a unique forum for researchers in this area to come together and discuss various applications and approaches to spatial modeling.

REFERENCES

• 1: R. B. Banks. Growth and Diffusion Phenomena : Mathematical Frameworks and Applications. Springer-Verlag, Berlin, Germany, 1994.
• 2: C. L. Barrett et al. TRANSIMS: Transportation Analysis and Simulation System. Technical Report LA-UR-00-1725, Los Alomos National Laboratory, 2001. Unclassified Report.
• 3: G. Bormann, F. Brosens, and E. De Schutter. Computational Modeling of Genetic and Biochemical Networks, chapter Modeling molecular diffusion, pages 189-224. Reviews in the Neurosciences. MIT Press, Boston, Mass. USA, 2001.
• 4: Christopher D. Carothers , Kaylan S. Perumalla , Richard M. Fujimoto, Efficient optimistic parallel simulations using reverse computation, Proceedings of the thirteenth workshop on Parallel and distributed simulation, p.126-135, May 01-04, 1999, Atlanta, Georgia, United States
• 5: R. Durrett and S. A. Levin. The importance of being discrete (and spatial). Theoretical Population Biology, 46:363-394, 1994.
• 6: J. Flaherty, M. Dindar, R. Loy, M. Shephard, B. Szymanski, J. Teresco, and L. Ziantz. Numerical Analysis, chapter An adaptive and parallel framework for partial differential equations. Addison Wesley Longman, Edinburgh, UK, 1998.
• 7: Richard M. Fujimoto, Parallel discrete event simulation, Communications of the ACM, v.33 n.10, p.30-53, Oct. 1990 [doi>10.1145/84537.84545]
• 8: David R. Jefferson, Virtual time, ACM Transactions on Programming Languages and Systems (TOPLAS), v.7 n.3, p.404-425, July 1985 [doi>10.1145/3916.3988]
• 9: Y.-B. Lin and P. Fishwick. Asynchronous parallel and distributed simulation (for PCS). IEEE Transactions on Systems, Man, and Cybgernetics, 26(4), 1996. University of Florita Tech Report tr95-005.
• 10: C. E. Perkins, editor. Ad Hoc Networking. Addison-Wesley, 1st edition, 2000.
• 11: B. Szabó and I. Babuška. Finite Element Analysis. John Wiley and Sons, Ltd., New York, London, Sydney, 1991.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.0 General

General Terms:
Design, Experimentation, Theory