We present two technologies for speeding up co-simulations under the FMI standards. By smoothing the input signals inside each FMU, the internal integrator may avoid re-initialization. This can significantly reduce the number of model and Jacobian evaluations. To further help the integrator we also propose a predictor compensation technique tailored to the input smoother. The main benefit of our technologies is the ease-of-use, requiring no model manipulations, nor any special co-simulation master algorithms. The technologies are implemented in Dymola~2025x and validated with both an academic mechanical model as well as thermo-fluid examples where we can observe performance gains with factor up to 100, and often around 5-10. One of these thermo-fluid examples is used in the \emph{OpenSCALING} research project to generate training data for constructing surrogate models, for which the input smoothing is especially important to speed up the dataset creation.

Uncertainty Quantification (UQ) studies allow us to determine whether a model is fit for a particular purpose, as well as the operational domain in which it can be used. Standardising the UQ analysis setup and result summary enables the iterative composition of UQ information, which is a crucial step in evaluating model credibility. In this paper, we present an initial attempt to specify UQ information as a cross-layer standard for Modelica-, FMI-, and SSP-based workflows subject to two essential restrictions: (a) uncertainties can only be described in terms of parameters, and (b) analysis is limited to forward uncertainty propagation and sensitivity analysis of nonlinear models. More analysis features are planned for the future. The approach is illustrated using both a simple example and an industrial use case.

Extended abstract for a User Presentation at the SSP User Meeting. The potential of the SSP standard to describe system structures to drive an end-to-end credible simulation process from the definition of an abstract analysis architecture to the evaluation of the overall system behavior in a co-simulation setup, is evaluated in this application to a heat pump system. From practitioners perspective the benefits and short-comings are compared against current best practices using proprietary solutions.