# A Model-Driven Approach for Conducting Simulation Experiments
## 1 Introduction
By building various kinds of meta models and storing them in repositories, we allow modelers and developers to share knowledge about simulation experiments between the different communities, e.g., finite element analysis (FEA) or virtual prototyping. These usually develop their simulation methodology and tooling independently, although the way simulation experiments are specified and conducted rarely differ and, thus, could be supported by the same pipeline. * _Increasing productivity and quality for complex experiments_: For novice modelers, MDE clearly facilitates the specification of simulation experiments via targeted graphical user interfaces (GUIs) and code generation. We provide additional support via an experiment validator and allow for the support of complex simulation experiments via meta model composition. Due to the focus on complex experiments, also experienced modelers can benefit. * _Reusability_: The meta models give meaning to the ingredients of simulation experiments. Together with an easy-to-use, tool-independent format and a variety of backend bindings, we support the flexible and automatic reuse of simulation experiments. This is of interest when the performance of different simulation tools needs to be compared or model alternatives implemented using different modeling and simulation approaches shall be evaluated. * _Automation_: Further automation of simulation experiments can be achieved if the knowledge about the various simulation experiments (given by meta models) is put into relation with context knowledge about the simulation study (e.g., given by conceptual models [16], provenance [17], or documentation [18]), as in these cases, simulation experiment specifications may be automatically generated and executed [19]. As our approach provides a means for integration with various other frameworks, it presents a valuable basis for further automation of simulation experiments. We demonstrate the above features and benefits of the approach using five experiments from three actual simulation studies. The studies are part of the Collaborative Research Centre (CRC) 1270 ELAINE ([[https://www.elaine.uni-rostock.de/en/](https://www.elaine.uni-rostock.de/en/)]([https://www.elaine.uni-rostock.de/en/](https://www.elaine.uni-rostock.de/en/)), accessed on 26 July 2022), which aims to develop novel electrically active implants for bone and cartilage regeneration, as well as for deep brain stimulation. As the research project is of an interdisciplinary nature, in each of the three studies, a different modeling and simulation approach and application domain are in focus (i.e., stochastic discrete-event simulation (DES) of a cell signaling pathway, virtual prototyping of a neurostimulator, and FEA of electric fields). The paper is organized as follows. In Section 2, we provide an overview of the related work. In Section 3, we present the design of our novel model-driven approach for conducting simulation experiments. Details of the open-source implementation are given in Section 4. In Section 5, we demonstrate the benefits of our approach using three real simulation studies. Finally, we end the paper with conclusions and future work in Section 6.