On the terminology and structuring of co-simulation methods

Hafner, Irene; Popper, Niki

doi:10.1145/3158191.3158203

Cited by 19 publications

(27 citation statements)

References 33 publications

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“…A pragmatic solution, called co-simulation (Gomes et al, 2018b;Hafner and Popper, 2017), is to perform the model integration at the dynamic behavioral level, where each model is used to produce a black box that consumes inputs and produces outputs over time. These black boxes, each representing the behavior of a subsystem/domain, can then be interconnected to mimic the interconnections of the corresponding subsystems.…”

Section: Discussionmentioning

confidence: 99%

Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

2019

Linköping Electronic Conference Proceedings

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Simulation Led Innovation at Procter & GambleThe required pace of innovation continues to increase in the consumer packaged goods industry. Increasingly historical approaches of development by physical prototypes lead to slow execution, high costs and incremental innovations.By leveraging simulations we can deliver better innovations, faster, at lower costs. Succeeding on this journey requires more than just technical mastery. Changing the way that innovation is done also requires changing how decisions are made and how resources are allocated. It is also necessary to think about the entire lifecycle of the digital assets such as simulation platforms in a manner similar to how we manage our physical assets (e.g. pilot plants). Digital assets required continued funding, maintenance and upgrades to remain viable over time.

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Section: Discussionmentioning

confidence: 99%

Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

2019

Linköping Electronic Conference Proceedings

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“…This has motivated researcher to conduct survey work and examine fundamental concepts of co-simulation and the terminology used. A discussion of differences in terminology and an attempt to classify and structure co-simulation methods was made by Hafner and Popper [29]. The authors Subject area for publications with the keyword "Co-Simulation" Figure 2: Subject area for publications that include the keyword "co-simulation" [48] 2008-2011 Improve the design of embedded software in vehicles.…”

Section: State Of the Artmentioning

confidence: 99%

An empirical survey on co-simulation: Promising standards, challenges and research needs

Schweiger

Gomes

Engel

et al. 2019

Simulation Modelling Practice and Theory

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Co-simulation is a promising approach for the modelling and simulation of complex systems, that makes use of mature simulation tools in the respective domains. It has been applied in wildly different domains, oftentimes without a comprehensive study of the impact to the simulation results. As a consequence, over the recent years, researchers have set out to understand the essential challenges arising from the application of this technique. This paper complements the existing surveys in that the social and empirical aspects were addressed. More than 50 experts participated in a two-stage Delphi study to determine current challenges, research needs and promising standards and tools. Furthermore, an analysis of the strengths, weakness, opportunities and threats of co-simulation utilizing the analytic hierarchy process resulting in a SWOT-AHP analysis is presented. The empirical results of this study show that experts consider the FMI standard to be the most promising standard for continuous time, discrete event and hybrid co-simulation. The results of the SWOT-AHP analysis indicate that factors related to strengths and opportunities predominate. References

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“…In regard to the implementation of hybrid dynamical models, Co-Simulation, as discussed in Hafner and Popper (2017) and Huynh (2016) is not applicable. It investigates tools and requirements to coordinate simulation processes of coupled subsystems.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

Winkler¹

2020

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Ziel der Arbeit ist unterschiedliche Einsatzszenarien von neuronalen Feed-Forward Netzwerken in der hybriden Modellierung zu untersuchen. Im Speziellen konzentriert sich die Arbeit auf hybride dynamische Systeme. Dabei handelt es sich um kontinuierliche Vorgänge, welche durch diskrete Ereignisse Änderung im Verhalten aufzeigen. Dementsprechend basiert die Modellierung solcher Systeme auf einer Kombination aus diskreten und kontinuierlichen Methoden. Derzeitige Standards der hybriden Modellbildung werden überblicksmäßig zusammengefasst. In der Softwareentwicklung kommen vermehrt Machine Learning Algorithmen zur Anwendung. Ein signifikanter Anteil der dabei verwendeten Methoden sind künstliche neuronale Netze, welche vor allem im Bereich der Bild-und Datenverarbeitung eingesetzt werden. Der grundlegende Aufbau sowie unterschiedliche gängige Strukturen neuronaler Netze werden eingeführt. Aufbauend auf die Gebiete Machine Learning und hybride dynamische Systeme, wird ein Framework vorgestellt, welches es ermöglicht, einzelne Elemente hybrider Modelle durch neuronale Netzwerke zu ersetzen. Drei unterschiedliche Anwendungsszenarien werden dabei untersucht: Die Approximation des dynamischen Verhaltens, die Vorhersage der diskreten Prozesse sowie der Ersatz des gesamten hybriden Systems durch neuronale Netze. Das Framework wird eingesetzt, um die drei definierten Szenarien anhand von zwei Beispielen auf Machbarkeit zu untersuchen. Es wird sich zeigen, dass die Approximation einzelner lokaler dynamischer Prozesse mittels speziellen neuronalen Netzwerken möglich ist. Auch eine Approximation des gesamten hybriden Systems durch ein neuronales Netz kann unter speziellen Voraussetzungen zum Ziel führen. Für eine gesamte Zeitreihe des Systemzustandes werden bereits etablierte Methoden mit Hilfe des Frameworks charakterisiert. Im Bezug auf diskrete Ereignisse in hybriden Systemen kann gezeigt werden, dass sich neuronale Netze für die Klassifikation des Systemzustandes, hinsichtlich des Auftretens eines Events, nicht eignen. Abseits von Anwendungen in der Modellierung hybrider Systeme kann das Framework eingesetzt werden, um Feed-Forward Netzwerke zu charakterisieren.

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On the terminology and structuring of co-simulation methods

Cited by 19 publications

References 33 publications

Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

An empirical survey on co-simulation: Promising standards, challenges and research needs

A Framework Including Artificial Neural Networks in Modelling Hybrid Dynamical Systems

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