This paper will present a new unified algorithm for unit checking and inference, and showing the benefits for various libraries.

The Modelica Language supports declaring units for variables using the SI-standard. This allows dimensional checking to detect possible errors in equations. The units for variables make it easier to interpret, input and plot their values. When we infer the unit of a variable we get the same benefits also for variables without a declared unit. We will use unit inference and checking for the combination, even if the check is primarily a dimensional check.

Both dimensional checking and unit inference are already implemented in several Modelica tools, but not consistently. The original motivation for this paper was to understand the different approaches, and demystify the unit handling with the goal of making it more available. Based on that understanding this paper will also present a new unified algorithm combining the different strengths, and showing the results for various libraries.

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.

The Functional Mock-up Interface (FMI) is the standard for exchanging industrial simulation models in a variety of different applications. Although sensitivity analysis for continuously differentiable systems is directly supported by the standard, for systems with state discontinuities, it is only possible to determine correct sensitivities to a limited extent. In this position paper, we investigate how sensitivity analysis for discontinuous Functional Mock-up Units (FMUs), i.e. including state and time events, works in theory and which additional steps are required to obtain correct results in practice. We further investigate that these steps are unnecessarily computationally intensive from a mathematical point of view, but cannot be implemented in a more efficient way under the current restrictions of the standard. We therefore make a concrete proposal for the new layered standard sensitivity analysis (LS-SA) that remedies the current deficits of FMI in the sensitivity analysis of discontinuous systems. In this way, LS-SA opens FMI towards a variety of next-level applications — including (scientific) machine learning and optimal control — by providing fully differentiable FMUs under high computational performance.