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Interoperability of AI-enhanced digital twins

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dc.contributor.authorMemon, U.
dc.contributor.authorMayer, W.
dc.contributor.authorSelway, M.
dc.contributor.authorStumptner, M.
dc.date.issued2025
dc.description.abstractInteroperability is one of the biggest challenges when multiple digital twins are used in collaboration. Although attempts to standardise and define interfaces have made significant progress, real interoperability is still difficult to achieve. It is due to unstated assumptions, contextual factors, and quality characteristics not covered by conventional methods. This paper presents a composition framework that uses a meta-model to capture contextual factors and quality characteristics in a structured manner that is required for compatibility between the models. It is achieved by developing a meta-model that explicitly represents the quality characteristics that can be used to decide whether digital twin models can be validly composed. Validation of the approach is illustrated by examples showing how our approach identifies the issues that are otherwise hidden compatibility issues. This paper also provides an algorithm to provide reasoning logic for requirements assessment by making implicit assumptions and contextual factors explicit and enabling the composition of digital twin models to be more effective.
dc.identifier.citationJournal of Industrial Information Integration, 2025; 48(100961):1-21
dc.identifier.doi10.1016/j.jii.2025.100961
dc.identifier.issn2452-414X
dc.identifier.issn2452-414X
dc.identifier.urihttps://hdl.handle.net/11541.2/44759
dc.language.isoen
dc.publisherElsevier BV
dc.relation.fundingFuture Energy Exports CRC
dc.rightsCopyright 2025 Published by Elsevier Inc.
dc.source.urihttps://doi.org/10.1016/j.jii.2025.100961
dc.subjectinteroperability
dc.subjectdigital twin
dc.subjectcomposition
dc.subjectmeta-model
dc.titleInteroperability of AI-enhanced digital twins
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9917076201201831

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