A Model-Driven Approach for Conducting Simulation Experiments
With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/16/7977 |
| _version_ | 1827618221596868608 |
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| author | Pia Wilsdorf Jakob Heller Kai Budde Julius Zimmermann Tom Warnke Christian Haubelt Dirk Timmermann Ursula van Rienen Adelinde M. Uhrmacher |
| author_facet | Pia Wilsdorf Jakob Heller Kai Budde Julius Zimmermann Tom Warnke Christian Haubelt Dirk Timmermann Ursula van Rienen Adelinde M. Uhrmacher |
| author_sort | Pia Wilsdorf |
| collection | DOAJ |
| description | With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed. |
| first_indexed | 2024-03-09T10:02:37Z |
| format | Article |
| id | doaj.art-84e5162cd86146f397b51c27820818af |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:02:37Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-84e5162cd86146f397b51c27820818af2023-12-01T23:20:41ZengMDPI AGApplied Sciences2076-34172022-08-011216797710.3390/app12167977A Model-Driven Approach for Conducting Simulation ExperimentsPia Wilsdorf0Jakob Heller1Kai Budde2Julius Zimmermann3Tom Warnke4Christian Haubelt5Dirk Timmermann6Ursula van Rienen7Adelinde M. Uhrmacher8Institute for Visual and Analytic Computing, University of Rostock, 18051 Rostock, GermanyInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, 18051 Rostock, GermanyInstitute for Visual and Analytic Computing, University of Rostock, 18051 Rostock, GermanyInstitute of General Electrical Engineering, University of Rostock, 18051 Rostock, GermanyInstitute for Visual and Analytic Computing, University of Rostock, 18051 Rostock, GermanyInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, 18051 Rostock, GermanyInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, 18051 Rostock, GermanyInstitute of General Electrical Engineering, University of Rostock, 18051 Rostock, GermanyInstitute for Visual and Analytic Computing, University of Rostock, 18051 Rostock, GermanyWith the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed.https://www.mdpi.com/2076-3417/12/16/7977simulationmodelingmodel-driven engineeringreproducibilityreusabilitydesign of experiments |
| spellingShingle | Pia Wilsdorf Jakob Heller Kai Budde Julius Zimmermann Tom Warnke Christian Haubelt Dirk Timmermann Ursula van Rienen Adelinde M. Uhrmacher A Model-Driven Approach for Conducting Simulation Experiments Applied Sciences simulation modeling model-driven engineering reproducibility reusability design of experiments |
| title | A Model-Driven Approach for Conducting Simulation Experiments |
| title_full | A Model-Driven Approach for Conducting Simulation Experiments |
| title_fullStr | A Model-Driven Approach for Conducting Simulation Experiments |
| title_full_unstemmed | A Model-Driven Approach for Conducting Simulation Experiments |
| title_short | A Model-Driven Approach for Conducting Simulation Experiments |
| title_sort | model driven approach for conducting simulation experiments |
| topic | simulation modeling model-driven engineering reproducibility reusability design of experiments |
| url | https://www.mdpi.com/2076-3417/12/16/7977 |
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