# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation
### Automation  
In the previous subsection, we already saw one case of automation, i.e., we demonstrated that a well-designed MDE pipeline is the foundation for reusing simulation experiments automatically [19] and allows integrating with ontologies. Now, we go one step further and show that we can integrate this approach with other frameworks for supporting simulation studies as a whole, e.g., an artifact-based workflow (an IDE for simulation studies).  
In an artifact-based workflow, the central products of simulation studies are identified and made explicit as artifacts. These include the conceptual model, the simulation models, and the simulation experiments. Each artifact is characterized by stages a modeler can move through to achieve certain milestones and preconditions called guards [4]. Figure 7 shows the conceptual model artifact of an artifact-based workflow for FEA studies [73]. While moving through the stages of the conceptual model, the modeler specifies various meta-information about the model, such as the modeling objective, requirements, and input data, which are stored inside the artifact. This meta-information of the conceptual model artifact, as well as other artifacts, can be used in the automatic generation of simulation experiments. For instance, in [73], an FEA simulation study of an electrical stimulation chamber was conducted with assistance from the workflow. Once the user reaches the stage _Specifying simulation experiment_, the MDE-based experiment generation can be triggered. For instance, the information collected by the workflow could be used to automatically generate a convergence test for the model. The convergence of numerical methods is of high importance in order to retrieve meaningful results from a numerical simulation [74]. In a convergence experiment, the mesh is incrementally refined until the estimated discretization error lies below a given error threshold or until the maximum number of iterations is reached. A meta model for convergence experiments, therefore, requires the following inputs:  
* The region of interest;
* An error metric allowing to estimate the discretization error for a given region;
* The maximum number of iterations or an error threshold to control the error;
* An initial meshing hypothesis, i.e., the minimum and maximum size of the finite elements, to initialize the meshing algorithm.  
By connecting our command-line tool to the workflow system, some of these inputs required by the meta model can be filled automatically by extracting meta information from the artifacts, e.g., the region of interest and the error metric were specified as requirements in the conceptual model [4]. Other inputs are specific to convergence studies, e.g., the initial minimum and maximum size of the elements, and thus, either need to be entered manually by the user (e.g., by opening our GUI pre-filled with the extracted values) or are initialized with default values (specified in the experiment meta model).  
Figure 8 shows the results of the convergence experiment that was generated semi-automatically for the model of the electrical stimulation chamber. It shows how the observed variable (current at Electrode 1) converges with increasing degrees of freedom in the finite element model and that the refinement could terminate after the fourth iteration.