Uncertainty Quantification and Runtime Monitoring Using Environment-Aware Digital Twins

Woodcock, Jim orcid.org/0000-0001-7955-2702, Gomes, Cláudio, Macedo, Hugo Daniel et al. (1 more author) (2021) Uncertainty Quantification and Runtime Monitoring Using Environment-Aware Digital Twins. In: Margaria, Tiziana and Steffen, Bernhard, (eds.) Leveraging Applications of Formal Methods, Verification and Validation:Tools and Trends - 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Proceedings. 9th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2020, 20-30 Oct 2020 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GRC , pp. 72-87.

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Margaria, Tiziana
  • Steffen, Bernhard
Copyright, Publisher and Additional Information:

Funding Information: We acknowledge the European Union for funding the INTO-CPS project (Grant Agreement 644047), which developed the open tool chain and the INTO-CPS Application; the Poul Due Jensen Foundation that funded subsequent work on taking this forward towards the engineering of digital twins; and the European Union for funding the HUBCAP project (Grant Agreement 872698). We acknowledge support from the UK EPSRC for funding for the RoboCalc (EP/M025756/1) and RoboTest projects (EP/R025479/1). Finally, we acknowledge support from the Royal Society and National Natural Science Foundation of China for funding for the project Requirements Modelling for Cyber-Physical Systems IEC/NSFC/170319. Early versions of the ideas in this paper were presented to the Digital Twin Centre in Aarhus in December 2019 (twice) and to the RoboStar team in York in January 2020. We are grateful for their feedback. Publisher Copyright: © 2021, Springer Nature Switzerland AG.
Dates:
  • Published: 5 August 2021
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Computer Science (York)
Depositing User: Pure (York)
Date Deposited: 05 Oct 2022 14:16
Last Modified: 16 Oct 2024 11:15
Published Version: https://doi.org/10.1007/978-3-030-83723-5_6
Status: Published
Publisher: Springer
Series Name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Identification Number: 10.1007/978-3-030-83723-5_6
Related URLs:
Open Archives Initiative ID (OAI ID):

Download

Export

Statistics


CORE (COnnecting REpositories)