2025-09-09 –, Forum
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.
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-1998 Ph.D. in Numerical Analysis from Lund Institute of Technology 1998 "Runge-Kutta Solution of Initial Value Problems - Methods, Algorithms and Implementation".
1999- Worked at Dassault Systemes AB (earlier Dynasim) with Modelica and Dymola.
2018- Chair of MAP-Lang (Modelica Language).
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Oliver Lenord earned his Phd at the Mechatronics Institute of the Univ. of Duisburg, Germany. Since 2016 he is affiliated with Robert Bosch - Corporate Research. Formerly he led the simulation software development group at Bosch Rexroth and worked as product manager for Siemens PLM in California, USA. Currently he is leading the European ITEA4 project OpenSCALING (openscaling.org). Earlier he also initiated and led the German research project PHyMoS, concerned with integrating scientific machine learning with classical modeling and led the award winning ITEA3 project EMPHYSIS that delivered the new eFMI standard. He also serves as vice-chairman of the Open Source Modelica Consortium (OSMC).