Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions

 
: Rüden, Laura von; Mayer, Sebastian; Sifa, Rafet; Bauckhage, Christian; Garcke, Jochen

:
Volltext urn:nbn:de:0011-n-5930904 (342 KByte PDF)
MD5 Fingerprint: 40aca895ceb19e58700ec6f3f5537e59
(CC) by
Erstellt am: 16.6.2020


Berthold, Michael R.:
Advances in Intelligent Data Analysis XVIII. Proceedings : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020
Cham: Springer International Publishing, 2020 (Lecture Notes in Computer Science 12080)
ISBN: 978-3-030-44583-6 (Print)
ISBN: 978-3-030-44584-3 (Online)
ISBN: 978-3-030-44585-0
S.548-560
International Symposium on Intelligent Data Analysis (IDA) <18, 2020, Konstanz>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
Fraunhofer SCAI ()
machine learning; simulation; hybrid approaches

Abstract
In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems. In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems.

: http://publica.fraunhofer.de/dokumente/N-593090.html