# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation
### Improved Knowledge Sharing across Domains
#### 5.1.3 Meta Model for Finite Element Analysis in Electromagnetics  
FEA is a general method that is capable of treating complex geometries and accurately computing, e.g., the properties and effects of electric fields in deep brain stimulation. A partial differential equation describing electric fields in the frequency domain is solved (physical model). Thus, time-harmonic fields are described and only the quasi-static behavior of the system under investigation is considered. The 3D geometry of the system is modeled explicitly, and corresponding boundary conditions and material properties are directly linked to the different domains of the geometric model. As FEA is a completely different modeling and simulation approach, no parts from the previously defined base meta models can be reused, and a new one is developed. Due to the separation of the geometric model and physical model, the experiment meta model for FEA (see the Supplementary Material, Table S1) has two new components: geometric model and physical model. These are complemented by a simulation component that comprises information about the type of solver and the accuracy of the solution (given by the coarseness of the mesh). For the observation component, derived quantities can be specified, as well as coordinates at which to evaluate these quantities.  
Note that this first draft of the FEA experiment meta model was developed with the background of electromagnetics in mind. Future efforts should aim to support FEA more generally. In particular, the various inputs and constraints depending on the type of physics need to be identified. In addition, multiphysics applications where mechanics, electromagnetics, and thermodynamics are coupled are of potential interest. In each discipline, PDEs comprising material properties together with geometrical constraints are the basis of the modeling approach [57]. Hence, the current meta model can provide a basis for further discussions and adjustments.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & **Name** & **Description** & **Type** & **Choices** & **Default** & **Required** \\ \hline \multirow{7}{*}{**FEA**} & replications & Number of simulation replications & Integer, \textgreater{}0 & – & 1 & yes \\  & randomSeed & Initialize random number generation & Long, \textgreater{}0 & – & – & no \\  & parallelThreads & Number of parallel threads & Integer, \textgreater{}0 & – & 1 & no \\  & stopCondition & Type of stop condition & Alternative & – & – & yes \\  & \(\rightarrow\)stopTime & Stop at specific point of time & Real, \textgreater{}0 & – & – & yes \\  & \(\rightarrow\)stopExpression & Stop based on simulation state & String & – & – & yes \\  & timeStep & Time step of the simulator & Map & – & – & yes \\  & \(\bullet\)timeStepSize & Size of time step & Real & – & – & yes \\  & \(\bullet\)timeStepUnit & Unit of time step & String & s, ns, & – & yes \\ \hline \end{tabular}
\end{table}
Table 2: Modified meta model component for virtual prototyping of heterogeneous systems. In contrast to simulations that are only based on discrete events, here also, a time step has to be configured for the numerical integration. The rows describe the different input properties of the meta model. Sub-properties are denoted by “\(\bullet\)”. Alternative meta model parts are indicated by “\(\rightarrow\)”.