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TOPAS Model Fitting: A Tool for Parameter Identification of Dynamical Systems

In: Operations Research Proceedings 2023

Author

Listed:
  • Marek Wiesner

    (University of Bremen, Center for Industrial Mathematics
    TOPAS Industriemathematik Innovation gGmbH)

  • Kai Schäfer

    (TOPAS Industriemathematik Innovation gGmbH)

  • Petr Shulpyakov

    (TOPAS Industriemathematik Innovation gGmbH)

  • Christof Büskens

    (University of Bremen, Center for Industrial Mathematics
    TOPAS Industriemathematik Innovation gGmbH)

Abstract

The identification of parameters within dynamical systems using measurements is a well-understood but effortful endeavor. To simplify the required steps, we present TOPAS Model Fitting, a software that covers aspects such as measurement preprocessing, optimization-based parameter identification, and post-optimal system simulation in a user-friendly, graphical interface-based way. The tool provides established methods such as single and multiple shooting, full discretization, and gradient matching. In this paper, they are applied to an extensive set of test problems. As only their default configurations are used, the results demonstrate the tool’s ability to solve a wide range of problems. They reveal that multiple shooting and full discretization are to be preferred, while the necessity of deploying multiple methods also becomes evident.

Suggested Citation

  • Marek Wiesner & Kai Schäfer & Petr Shulpyakov & Christof Büskens, 2025. "TOPAS Model Fitting: A Tool for Parameter Identification of Dynamical Systems," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 201-207, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_26
    DOI: 10.1007/978-3-031-58405-3_26
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