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UID:pretalx-16thmodelicafmiconference-CLGH3S@pretalx.com
DTSTART;TZID=CET:20250909T102000
DTEND;TZID=CET:20250909T104500
DESCRIPTION:Hybrid modeling – the combination of first-principle models a
 nd machine learning – offers the potential to increase model accuracy wh
 ile reducing modeling effort. Although approaches for creating hybrid mode
 ls from system simulation models exist\, the unique characteristics of Mod
 elica-based\, object-oriented models – such as modularity and reusabilit
 y – can\, as of today\, not be utilized. In this contribution\, we explo
 re approaches for bridging this gap to enable the use of hybrid models wit
 h Modelica. Key challenges of architecture definition\, training environme
 nt and reintegration of the trained machine learning parts into a Modelica
  model are addressed. To illustrate our approach\, we present a case study
  involving a SCARA robot. This example demonstrates a partially integrated
  workflow for hybrid modeling\, intended to serve as a foundation and moti
 vation for further research.
DTSTAMP:20260715T134957Z
LOCATION:Audi-Midi
SUMMARY:Towards Integration of PeN-ODEs in a Modelica-based workflow - Lars
  Mikelsons\, Andreas Hofmann
URL:https://pretalx.com/16thmodelicafmiconference/talk/CLGH3S/
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UID:pretalx-16thmodelicafmiconference-THVTE8@pretalx.com
DTSTART;TZID=CET:20250909T125900
DTEND;TZID=CET:20250909T130000
DESCRIPTION:This session is chaired by
DTSTAMP:20260715T134957Z
LOCATION:Audi-Midi
SUMMARY:Session Chair - Lars Mikelsons
URL:https://pretalx.com/16thmodelicafmiconference/talk/THVTE8/
END:VEVENT
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UID:pretalx-16thmodelicafmiconference-RMSZFE@pretalx.com
DTSTART;TZID=CET:20250909T132500
DTEND;TZID=CET:20250909T135000
DESCRIPTION:The Functional Mock-up Interface (FMI) is the standard for exch
 anging 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 discontinuiti
 es\, it is only possible to determine correct sensitivities to a limited e
 xtent. In this position paper\, we investigate how sensitivity analysis fo
 r 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 step
 s are unnecessarily computationally intensive from a mathematical point of
  view\, but cannot be implemented in a more efficient way under the curren
 t restrictions of the standard. We therefore make a concrete proposal for 
 the new layered standard sensitivity analysis (LS-SA) that remedies the cu
 rrent deficits of FMI in the sensitivity analysis of discontinuous systems
 . In this way\, LS-SA opens FMI towards a variety of next-level applicatio
 ns — including (scientific) machine learning and optimal control — by 
 providing fully differentiable FMUs under high computational performance.
DTSTAMP:20260715T134957Z
LOCATION:Forum
SUMMARY:LS-SA: Developing an FMI layered standard for holistic & efficient 
 sensitivity analysis of FMUs - Tobias Thummerer\, Lars Mikelsons\, Hans Ol
 sson\, Chen Song\, Torsten Blochwitz\, Julia Gundermann
URL:https://pretalx.com/16thmodelicafmiconference/talk/RMSZFE/
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