## 4 Discussion
### Future Work  
The current mosaik scheduling algorithm can, as mentioned above, be interpreted as a flexible discrete time scheduling. While such a solution works well for a variety of different application cases, it fails to fully support some crucial aspects of CPES, like communication systems. In the time discrete scheduling, each step performed by a simulator predicts the time of the next step. A communication simulation, however, introduces a delay into a system's message exchange. As a consequence, a simulator may not be able to predict its next step on its own but rather needs information from other simulators. The only way of handling this problem in a discrete time setup is to reduce the step size for simulators to the length of the minimal possible delay and check during each step whether data has been received. A more efficient solution, on the other hand, is given by the employment of DES scheduling that allows simulators to be triggered by events and stay idle until then. Such a scheduling option is currently developed for mosaik. It is planned to be available as an additional scheduling module for the framework that is interoperable with the current scheduling (similar to the approach seen in Ptolemy II). It has been discussed in [46] (and above) that different co-simulation frameworks may be more suitable for different application cases. As an example, mosaik provides benefits for co-simulation prototyping, but may fall short on strong real-time requirements in some hardware integrated test cases. Therefore, it is planned to have the assets of the outlined testing environment (simulation planning, UQ, ISAAC) be interoperable with other frameworks, e.g., HLA-based systems. Accordingly, a tool-invariant process for test realization is currently developed that will include a classification of co-simulation services to find the best framework for a given application case. Despite mosaik's focus on usability, the improvement of the platform performance is also an ongoing research field. Since co-simulation performance is highly dependent on the employed simulators, an approach is currently developed that allows the integration of surrogate models into co-simulation. In other words, machine learning algorithms are trained to replicate the input-output relationship of more complex simulation models. This way computationally expensive simulators or sets of simulators can be replaced by surrogates to boost the overall process performance. UQ methods are used to ensure a sufficient accuracy in the resulting co-simulation. The outlined UQ system MoReSQUE is so far limited to analyzing the impact of simulation input uncertainties on the output accuracy. However, there are other aspects of co-simulation uncertainty that may play an important role for the assessment of result reliability. On the one hand, coupling uncertainty between simulators has to be analyzed, e.g., resulting from different model resolutions. Additionally, simulator-internal uncertainty has to be assessed via model validation techniques. This is typically done by comparing simulator output with measurement data or other simulators in a cross validation. However, additional research is needed on methods of model uncertainty representation in co-simulation as well as the combination of validation information from different sources due to the interdisciplinary nature of CPES. These topics will form the next steps in UQ research for the outlined testing environment. Next to that, improved approaches are needed for the integration of expert knowledge into the UQ process. One possible solution is the availability of user-friendly tools for uncertainty modeling as presented in [47]. For a more sophisticated collaboration process, it is planned to include UQ considerations directly into the information model presented in Section 2.3. Finally, validation of UQ with measurement data has to be researched more thoroughly. Such approaches are so far sparse in the energy field due to the limited availability of measurement data from complex CPES.