Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36653
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dc.contributor.authorBogomolov, Sergiyen_UK
dc.contributor.authorGomes, Cláudioen_UK
dc.contributor.authorIsasa, Carlosen_UK
dc.contributor.authorSoudjani, Sadeghen_UK
dc.contributor.authorStankaitis, Pauliusen_UK
dc.contributor.authorWright, Thomasen_UK
dc.date.accessioned2025-03-05T01:28:23Z-
dc.date.available2025-03-05T01:28:23Z-
dc.date.issued2024-07-25en_UK
dc.identifier.urihttp://hdl.handle.net/1893/36653-
dc.description.abstractDigital twin is a technology that facilitates a real-time coupling of a cyber–physical system and its virtual representation. The technology is applicable to a variety of domains and facilitates more intelligent and dependable system design and operation, but it relies heavily on the existence of digital models that can be depended upon. In realistic systems, there is no single monolithic digital model of the system. Instead, the system is broken into subsystems, with models exported from different tools corresponding to each subsystem. In this paper, we focus on techniques that can be used for a black-box model, such as the ones implementing the Functional Mock-up Interface (FMI) standard, formal analysis, and verification. We propose two techniques for simulation-based reachability analysis of models. The first one is based on system dynamics, while the second one utilizes dynamic sensitivity analysis to improve the quality of the results. Our techniques employ simulations to obtain the model’s sensitivity with respect to the initial state (or model’s Lipschitz constant) which is then used to compute reachable states of the system. The approaches also provide probabilistic guarantees on the accuracy of the computed reachable sets that are based on simulations. Each technique requires different levels of information about the black-box system, allowing the readers to select the best technique according to the capabilities of the models. The validation experiments have demonstrated that our proposed algorithms compute accurate reachable sets of stable and unstable linear systems. The approach based on dynamic sensitivity provides an accurate and, with respect to system dimensions, more scalable approach, while the sampling-based method allows a flexible trade-off between accuracy and runtime cost. The validation results also show that our approaches are promising even when applied to nonlinear systems, especially, when applied to larger and more complex systems. The reproducibility package with code and data can be found at https://github.com/twright/FMI-Reachability-Reproducibility.en_UK
dc.language.isoenen_UK
dc.publisherSAGE Publicationsen_UK
dc.relationBogomolov S, Gomes C, Isasa C, Soudjani S, Stankaitis P & Wright T (2024) Reachability analysis of FMI models using data-driven dynamic sensitivity. <i>SIMULATION</i>. https://doi.org/10.1177/00375497241261409en_UK
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 Lficense (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage)en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectReachability analysisen_UK
dc.subjectdigital twinsen_UK
dc.subjectFunctional Mock-up Interfaceen_UK
dc.subjectdynamic sensitivity equationsen_UK
dc.subjectLipschitz constanten_UK
dc.titleReachability analysis of FMI models using data-driven dynamic sensitivityen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1177/00375497241261409en_UK
dc.citation.jtitleSIMULATIONen_UK
dc.citation.issn1741-3133en_UK
dc.citation.issn0037-5497en_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailpaulius.stankaitis@stir.ac.uken_UK
dc.citation.date25/07/2024en_UK
dc.contributor.affiliationNewcastle Universityen_UK
dc.contributor.affiliationAarhus Universityen_UK
dc.contributor.affiliationAarhus Universityen_UK
dc.contributor.affiliationMax Planck Institute for Software Systemsen_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationAarhus Universityen_UK
dc.identifier.isiWOS:001276825100001en_UK
dc.identifier.scopusid85200044022en_UK
dc.identifier.wtid2032414en_UK
dc.date.accepted2024-03-18en_UK
dcterms.dateAccepted2024-03-18en_UK
dc.date.filedepositdate2025-02-21en_UK
rioxxterms.apcpaiden_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorBogomolov, Sergiy|en_UK
local.rioxx.authorGomes, Cláudio|en_UK
local.rioxx.authorIsasa, Carlos|en_UK
local.rioxx.authorSoudjani, Sadegh|en_UK
local.rioxx.authorStankaitis, Paulius|en_UK
local.rioxx.authorWright, Thomas|en_UK
local.rioxx.projectProject ID unknown|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2025-02-21en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2025-02-21|en_UK
local.rioxx.filenamebogomolov-et-al-2024-reachability-analysis-of-fmi-models-using-data-driven-dynamic-sensitivity.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1741-3133en_UK
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