Categorizing methods for integrating machine learning with executable specifications

Harel, David; Yerushalmi, Raz; Marron, Assaf; Elyasaf, Achiya

doi:10.1007/s11432-022-3826-6

Sci. China Inf. Sci.

2023

DOI: 10.1007/s11432-022-3826-6

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Categorizing methods for integrating machine learning with executable specifications

David Harel,

Raz Yerushalmi,

Assaf Marron

et al.

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2024

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Digital Twins Verification and Validation Approach through the Quintuple Helix Conceptual Framework

Perisic,

Perisic

2024

Electronics

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The concept of digital twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber–physical systems. Despite the various foundations, standards, and platforms in systems engineering, there are ongoing challenges with verification and validation methodology. This study aims to establish a generic framework that addresses the various aspects of digital twinning. The multifaceted nature of the problem requires raising the abstraction level in both the real (actual) and virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the quintuple helix model with the problem and operational domains of a real (actual) twin, the solution and implementation domains of a virtual twin, and the execution domain as the bridge that links them. Verification and validation dimensions follow the meta object facility abstraction layers (instance, model, meta-model, and meta-meta-model) mapping over five helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios. The application of main conceptual framework mechanisms in a real-world example study aids the verification of this research’s intentions. The validation is a matter of further research endeavors.

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Digital Twins Verification and Validation Approach through the Quintuple Helix Conceptual Framework

Perisic,

Perisic

2024

Electronics

View full text Add to dashboard Cite

show abstract

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Categorizing methods for integrating machine learning with executable specifications

Cited by 1 publication

References 50 publications

Digital Twins Verification and Validation Approach through the Quintuple Helix Conceptual Framework

Digital Twins Verification and Validation Approach through the Quintuple Helix Conceptual Framework

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