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Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms

Author

Listed:
  • Schweiger, G.
  • Nilsson, H.
  • Schoeggl, J.
  • Birk, W.
  • Posch, A.

Abstract

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper complements existing surveys on large-scale modeling and simulation of physical systems by conducting expert surveys. We conducted a two-stage empirical survey in order to investigate research needs, current challenges as well as promising modeling and simulation paradigms. Furthermore, we applied the analytic hierarchy process method to prioritise the strengths and weakness of different modeling paradigms. The results of this study show that experts consider acausal modeling techniques to be suitable for modeling large scale systems, while causal techniques are considered less suitable.

Suggested Citation

  • Schweiger, G. & Nilsson, H. & Schoeggl, J. & Birk, W. & Posch, A., 2020. "Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319307052
    DOI: 10.1016/j.amc.2019.124713
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    Citations

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    Cited by:

    1. Kofman, Ernesto & Fernández, Joaquín & Marzorati, Denise, 2021. "Compact sparse symbolic Jacobian computation in large systems of ODEs," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    2. Li, Jiajia & Tian, Xin & Wei, Guoliang, 2022. "Asynchronous partially mode-dependent control for switched larger-scale nonlinear systems with bounded sojourn time," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    3. Gerald Schweiger & Fabian Kuttin & Alfred Posch, 2019. "District Heating Systems: An Analysis of Strengths, Weaknesses, Opportunities, and Threats of the 4GDH," Energies, MDPI, vol. 12(24), pages 1-15, December.
    4. Marzorati, Denise & Fernández, Joaquin & Kofman, Ernesto, 2022. "Efficient connection processing in equation–based object–oriented models," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    5. Arellano-García, María Evarista & Camacho-Gutiérrez, José Ariel & Solorza-Calderón, Selene, 2021. "Machine learning approach for higher-order interactions detection to ecological communities management," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    6. Falay, Basak & Schweiger, Gerald & O’Donovan, Keith & Leusbrock, Ingo, 2020. "Enabling large-scale dynamic simulations and reducing model complexity of district heating and cooling systems by aggregation," Energy, Elsevier, vol. 209(C).

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