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Architecture for Digital Twin-based Reinforcement Learning Optimization of Cyber-Physical Systems

Title: Architecture for Digital Twin-based Reinforcement Learning Optimization of Cyber-Physical Systems
Authors: Modrakowski, Elias; Braun, Niklas; Hajnorouzi, Mehrnoush; Eich, Dr. Andreas; Javaheri, Narges; Doornbos, Dr. ir. Richard; Moritz, Sebastian; Bikker, Jan-Willem; van Beek, Rutger
Publication Year: 2023
Collection: German Aerospace Center: elib - DLR electronic library
Subject Terms: Application and Evaluation
Description: The optimization of complex cyber-physical systems is a crucial task for their correct functioning, usability, and commercial success. Due to their complexity, scale and resource intensiveness, conventional manual optimization is infeasible in many instances. We investigate the combination of the Digital Twin paradigm and Reinforcement Learning framework to address the long response times, limited availability of data, and the intractability of such systems. Here, the Digital Twin functions as the training environment in different development phases of the optimization. In this position paper we showcase our ongoing research on developing a reference architecture of a Digital Twin-AI optimization system. This includes presenting the development process of the optimization system in terms of phases and the consequent architectural viewpoints
Document Type: conference object
File Description: application/pdf
Language: English
Relation: https://elib.dlr.de/197458/2/TwinArch_127_EMr.pdf; Modrakowski, Elias und Braun, Niklas und Hajnorouzi, Mehrnoush und Eich, Dr. Andreas und Javaheri, Narges und Doornbos, Dr. ir. Richard und Moritz, Sebastian und Bikker, Jan-Willem und van Beek, Rutger (2023) Architecture for Digital Twin-based Reinforcement Learning Optimization of Cyber-Physical Systems. TwinArch Workshop @ ECSA 2023, 2023-09-18, Istanbul, Türkei.
Availability: https://elib.dlr.de/197458/; https://elib.dlr.de/197458/2/TwinArch_127_EMr.pdf
Accession Number: edsbas.E438B138
Database: BASE