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Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A unified TEVV framework can standardize how digital twins are tested, evaluated, verified, and validated across four industries.

desk verdict A useful, well-organized TEVV survey for digital twins whose headline 'empirical validation' rests on four illustrative case studies with unbacked numbers. read the letter →

arxiv 2507.04555 v1 pith:S7IDD3WK submitted 2025-07-06 cs.SE

Gabriella Waters This is my paper · ORCID
classification cs.SE
keywords digitaltwinsTEVVverificationandvalidationontologycertificationethicalconsiderationscasestudies
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the reliability of digital twins, live virtual models of physical systems, can be governed by one standardized framework for testing, evaluation, verification, and validation (TEVV). It argues that today's digital twins lack systematic quality assurance and that their live, data-driven, and ethically sensitive nature demands dedicated methods rather than traditional model-validation routines. The payoff would be interoperable, comparable, and certifiable digital twins that organizations and regulators can trust for operational decisions. To show the framework working, the paper offers case studies in manufacturing, healthcare, finance, and urban planning, each with a TEVV plan and stated quantitative improvements.

What carries the argument

The load-bearing mechanism is the four-dimensional TEVV structure: testing (unit, integration, system, simulation), evaluation (performance, usability, utility, value, comparative), verification (requirements, data, model, behavior), and validation (empirical, predictive, operational, conceptual). The paper anchors this structure in a Core Digital Twin Ontology (CDTO) that names the shared elements of any twin, physical entity, virtual entity, data, model, interface, simulation, and visualization, and in domain-specific ontologies that give each industry a common vocabulary for validation. Standardized metrics, reporting templates, a phased planning-to-continuous-improvement workflow, and a certification process with pre-assessment, third-party execution, compliance evaluation, and periodic reassessment supply the operational layer that the TEVV taxonomy alone would lack.

What would settle it

Look for the data behind the reported outcomes, 15% maintenance-prediction gain, 22% emergency-response gain, 18% risk-assessment gain, 25% false-positive reduction, and 30% reporting-efficiency gain, in the manuscript or its supplements; the empirical-validation claim is settled by whether that data and a reproducible measurement protocol exist.

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Extended reading notes

Core claim

The paper's central claim is that the trustworthiness of a digital twin should be established through a dedicated, standardized lifecycle of testing, evaluation, verification, and validation, rather than through ad hoc or traditional model checks alone. It argues that because digital twins are live, data-driven, multi-modal systems used in critical decision-making, they need TEVV methods that cover unit-to-system testing, performance, usability, utility, and value evaluation, requirements, data, model, and behavior verification, and empirical, predictive, operational, and conceptual validation. To make the framework operational, the paper defines a Core Digital Twin Ontology (CDTO) with domain extensions for manufacturing, healthcare, finance, urban planning, and aerospace, plus standardized metrics, reporting templates, and a tiered certification process with third-party audits. It also folds ethical requirements such as privacy, fairness, transparency, accountability, and environmental impact directly into the validation criteria. The paper claims to demonstrate the framework's applicability through four domain case studies, each reporting improved outcomes such as a 15% gain in maintenance-prediction accuracy and a 22% gain in emergency-response prediction accuracy.

Load-bearing premise

The load-bearing premise is that the four case studies are actual empirical demonstrations, not just illustrative stories; if their percentage improvements are imagined rather than measured against a real system, the paper's empirical-validation claim gives way.

Editorial extensions

If this is right

  • Following the framework should give organizations a repeatable method for judging whether a digital twin is accurate, usable, and valuable, with quantitative cutoffs such as MAE, RMSE, R², response time, and ROI.
  • If consistently applied, standardized TEVV should allow digital twins from different vendors and domains to be compared and to interoperate, because they share the same validation vocabulary and data-exchange formats.
  • Ethical requirements such as privacy, fairness, transparency, accountability, and environmental impact become formal validation criteria rather than optional additions to a digital-twin project.
  • The four case studies project concrete benefits: 15% higher maintenance-prediction accuracy in manufacturing, 22% higher emergency-response prediction accuracy in healthcare, 18% better real-time risk assessment and 25% fewer false-positive fraud alerts in finance, and efficiency improvements in urban traffic and environmental planning.
  • A tiered certification system with third-party audits would give buyers and regulators a common trust signal for digital twins.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test is to run the framework's own predictive-validation tools, such as backtesting, cross-validation, and out-of-sample testing, on the four case-study domains and publish the resulting data, which would convert the framed percentages into verified measurements.
  • The utility-over-usability effect named in the paper invites a direct experiment: measure task completion, error rate, and satisfaction for a high-utility, low-usability twin versus a balanced twin to see when users tolerate friction for value.
  • The ontology layer could be tested for interoperability by having two independently built digital twins, one in manufacturing and one in urban planning, exchange data through CDTO-aligned schemas; the paper does not demonstrate such an exchange.
  • A regulatory mapping from the framework's phases to specific obligations in medical, aviation, and financial regulation would be a concrete extension that the paper only gestures toward.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a comprehensive framework for testing, evaluation, verification, and validation (TEVV) of digital twins. It introduces a taxonomy of digital twin types and characteristics, domain-specific ontologies, and a TEVV methodology organized into testing, evaluation, verification, and validation. It also discusses standardization, metrics, certification, ethical considerations, and provides four case studies in manufacturing, healthcare, financial services, and urban planning that are claimed to demonstrate the framework's applicability. The paper concludes with limitations, broader impacts, and research directions.

Significance. If the framework were empirically supported, it could provide a useful reference for practitioners and contribute to the ongoing standardization of digital twin verification and validation. The paper usefully synthesizes a broad literature, including recent work on digital twin credibility and uncertainty quantification, and integrates ethical considerations into the TEVV process. However, the central empirical-validation claim is not supported by the evidence presented. The four case studies are narrative illustrations with unsupported quantitative outcomes, and the framework is not compared with existing standards or frameworks. As presented, the contribution is closer to a structured checklist or survey than to a validated comprehensive framework. The paper may have value as a practice-oriented overview, but that value is undermined by the overstatement of empirical support.

major comments (4)
  1. [Case Studies (pp. 30–33); Objectives and Contributions (p. 3)] The paper lists 'Empirical Validation through Case Studies' as a primary objective and contribution, but the four case studies are introduced as examples 'to illustrate the application' and provide no dataset, baseline measurement, evaluation protocol, statistical test, or reference to a real or simulated system. The 'Outcomes' list specific quantitative gains (e.g., 15% maintenance prediction accuracy, 22% emergency response time prediction, 18% risk assessment accuracy, 25% false-positive reduction, 30% regulatory reporting improvement) with no description of how these figures were derived. These numbers are unsupported and cannot serve as evidence of empirical validation. This is load-bearing because the abstract and introduction state that the paper's contribution is a comprehensive framework and its empirical validation through diverse case studies.
  2. [Standardization of TEVV Approaches (pp. 17–25)] The paper claims the framework is 'comprehensive' and 'standardized', but it never defines criteria for comprehensiveness nor compares the proposed framework with existing TEVV/credibility standards such as NIST SP 1500-21, ASME V&V 40, or ISO/IEC 15288, despite citing some of these works (refs 13, 34, 35). A gap analysis or comparative table would be needed to substantiate the claim that this framework addresses 'the absence of standardized TEVV methodologies' and is more comprehensive than prior art. As it stands, the comprehensiveness claim is asserted rather than demonstrated.
  3. [Case Studies (pp. 30–33); Limitations (p. 33)] The Limitations section acknowledges data dependence and domain-specific challenges but never discloses that the case-study numbers are illustrative and not derived from actual deployments. The Case Studies section itself uses 'to illustrate the application', which is in tension with the paper's claim of empirical validation. At minimum, the authors should state explicitly that the case studies are hypothetical and do not constitute empirical validation; ideally, the paper should either remove the empirical-validation claim or replace the illustrative scenarios with real deployed case studies that include data and evaluation details.
  4. [Case Studies (pp. 30–33)] The case studies are constructed by applying the TEVV framework's categories (testing, evaluation, verification, validation) to invented scenarios, and then the same case studies are offered as evidence that the framework is applicable. This is circular: the narrative is generated by the framework itself, so it cannot independently test the framework's validity. Independent evidence, such as pre-registered deployments or comparisons against existing practice, would be needed.
minor comments (6)
  1. [Current TEVV Challenges in Digital Twin Development (p. 3)] The sentence 'as they process new data and update their internal states Ding & Xing, 2025)' is missing an opening parenthesis; it should read '(Ding & Xing, 2025).'
  2. [References] Reference 30 (Blair, 2025) identifies a paper that was published in Patterns in 2021 (DOI 10.1016/j.patter.2021.100359); the citation year and publication year are inconsistent and should be corrected.
  3. [Figure 1 (p. 2)] Figure 1 is referenced in the main text but the actual graphic is not included in the manuscript; please ensure the figure is present in the final version.
  4. [Tables 2 and 3] The table captions for Tables 2 and 3 state that the taxonomy is for digital twins 'in multiple application domains', but the tables themselves are generic; either provide domain-specific instantiations or adjust the captions.
  5. [Operationalizing the digital twin TEVV (pp. 26–27)] This section presents six phases as a list but lacks elaboration on how these phases interact or how priorities should be set; adding a brief example or decision flowchart would improve clarity.
  6. [References] Several references (e.g., refs 22, 29, 36, 38) have incomplete bibliographic details, including missing page numbers or author-name typos (e.g., 'N.n Liu' in ref 22); the reference list should be carefully proofread.

Circularity Check

1 steps flagged · score 5.0 of 10

The 'empirical validation through case studies' claim is self-referential: the only supporting evidence is the paper's own illustrative examples with asserted outcome percentages, so the validation reduces to the paper's construction.

  1. other [Section 'Case Studies', p. 30; 'Objectives and Contributions', pp. 3-4]
    "To illustrate the application of the proposed TEVV framework, three example case studies from different domains are presented: ... Outcomes: ... Improved prediction accuracy for maintenance needs by 15% ... Improved emergency response time prediction accuracy by 22% ... Improved real-time risk assessment accuracy by 18% ... Validated 25% reduction in false positive fraud alerts ... Demonstrated 30% improvement in regulatory reporting efficiency"

    The paper's stated contribution is 'Empirical Validation through Case Studies: Demonstration of the framework's applicability through comprehensive examples' (Objectives and Contributions). The only evidence offered for this contribution is a section explicitly introduced as illustrative: 'To illustrate the application of the proposed TEVV framework, three example case studies...'. These case studies are not reports of real deployments; each 'Outcomes' list contains specific quantitative gains (15%, 22%, 18%, 25%, 30%) with no dataset, baseline, evaluation protocol, statistical test, or external reference.

full rationale

No mathematical derivation chain or fitted-parameter prediction loop exists in this paper; the TEVV framework is presented as a qualitative taxonomy of testing, evaluation, verification, validation, metrics, ontologies, and ethical considerations, with no equations that reduce to their inputs and no self-citations used as load-bearing evidence. The circularity is localized to the 'empirical validation' claim. The Objectives list 'Empirical Validation through Case Studies' as a key contribution, and the abstract promises a comprehensive framework, but the Case Studies section explicitly says the cases are examples 'to illustrate the application' of the framework. The quantitative 'Outcomes' in each case study are asserted percentages without any data, baseline, methodology, or connection to a real or simulated system, so they cannot independently validate the framework. The empirical-validation claim is thus self-referential: the paper constructs illustrative scenarios, assigns favorable outcomes to them, and then presents those outcomes as evidence that the framework is applicable and effective. This is a partial circularity affecting the paper's strongest claim, though the framework itself, as a structured proposal and survey, retains independent descriptive content. Because the central framework does not depend on the case-study numbers, the score is moderate rather than severe.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters, invented entities, or mathematical axioms are present. The framework rests on domain assumptions about digital twin characteristics and the completeness of its categories.

assumptions (3)
  • domain assumption Digital twins have unique TEVV challenges that distinguish them from traditional modeling and simulation approaches.
    The motivation is based on this assertion; no formal or empirical evidence is given beyond citations.
  • domain assumption The classification of digital twins into component, asset, system/unit, process, and enterprise types is a sufficient basis for TEVV planning.
    The framework depends on this categorization, but it is presented without justification or validation.
  • domain assumption The listed ontologies adequately capture the necessary domain concepts for TEVV.
    Ontologies are described as lists of example classes and instances, not formalized or tested.

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Pith. "Pith review of Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework." pith.science (2026). https://pith.science/paper/S7IDD3WK

@misc{pith2026250704555,
  author       = {Pith},
  title        = {Pith review of: Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S7IDD3WK}},
  note         = {Machine review of arXiv:2507.04555}
}
read the original abstract

Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper presents a comprehensive framework for the Testing, Evaluation, Verification and Validation (TEVV) of digital twins to address the unique challenges posed by these dynamic and complex virtual models.

Figures

Figures reproduced from arXiv: 2507.04555 by the authors.

Figure 1
Figure 1. Digital Twin Lifecycle Loop highlighting the continuous feedback between a physical asset and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 29 canonical work pages

  1. [1]

    Digital Twin: enabling technologies, challenges and open research

    Fuller, A., Fan, Z., Day, C., Barlow, C. Digital Twin: enabling technologies, challenges and open research. (2020). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/document/9103025

    work page arXiv 2020

  2. [2]

    Digital twin in industry: State-of-the-Art

    Tao, F., Zhang, H., Liu, A., Nee, A. Digital twin in industry: State-of-the-Art. (2019, April 1). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/document/8477101

    work page arXiv 2019

  3. [3]

    Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002

    doi:10.1016/j.cirpj.2020.02.002 2020

  4. [4]

    Barricelli, B., Casiraghi, E., Fogli, D. (2019). A survey on Digital Twin: Definitions, characteristics, applications, and design implications. (2019). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/document/8901113

    arXiv 2019

  5. [5]

    Sel, K., Hawkins-Daarud, A., Chaudhuri, A., Osman, D., Bahai, A., Paydarfar, D., Willcox, K., Chung, C., & Jafari, R. (2025). Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01447-y

    doi:10.1038/s41746-025-01447-y 2025

  6. [6]

    Validation of digital twins: challenges and opportunities

    Hua, E., Lazarova-Molnar, S., Francis, D. Validation of digital twins: challenges and opportunities. (2022). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/document/10015420

    work page arXiv 2022

  7. [7]

    A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives

    Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022). A comprehensive review of Digital Twin -- Part 2: Roles of uncertainty quantification and Optimization, a battery Digital Twin, and Perspectives. arXiv (Preprint No. 2208.12904). https://arxiv.org/abs/2208.12904

    work page Pith review arXiv 2022

  8. [8]

    Lugaresi, G., Gangemi, S., Gazzoni, G., Matta, A. (2023). Online Validation of Digital Twins for Manufacturing Systems. Computers in Industry, 150. https://doi.org/10.1016/j.compind.2023.103942

    arXiv 2023

Show all 44 references
  1. [9]

    J., Douthwaite, J

    Somers, R. J., Douthwaite, J. A., Wagg, D. J., Walkinshaw, N., & Hierons, R. M. (2022). Digital-twin-based testing for cyber–physical systems: A systematic literature review. Information and Software Technology, 156, 107145. https://doi.org/10.1016/j.infsof.2022.107145

    2022

  2. [10]

    R., & Willcox, K

    Huang, L., Varshney, L. R., & Willcox, K. E. (2024). Formal Verification of Digital Twins with TLA and Information Leakage Control. arXiv.org. https://arxiv.org/abs/2411.18798 38

    2024 arXiv

  3. [11]

    Lu, H., Zhang, L., Wang, K., Huang, Z., Cheng, H., & Cui, J. (2024). A Framework for the Credibility Evaluation of Digital Twins. In Simulation foundations, methods and applications (pp. 69–93). https://doi.org/10.1007/978-3-031-69107-2_4

    2024 doi

  4. [12]

    Gao, T., Chen, L., Zhang, X., Guo, J., & Ni, D. (2025). Credibility Assessment for Digital Twins in Vehicle-in-the-Loop Test Based on Information Entropy. Sensors, 25(5), 1372. https://doi.org/10.3390/s25051372

    2025 doi

  5. [13]

    Shao, G., Kibira, D., Frechette, S. (2024). Credibility consideration for digital twins in manufacturing. (NIST SP 1500-21). National Institute of Standards and Technology. https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957417

    2024

  6. [14]

    Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., Xiao, Y., Syeda-Mahmood, T., Tuli, R., & Deng, J. (2024). Digital twins for health: a scoping review. Npj Digital Medicine, 7(1). https://doi.org/10.1038/s41746-024-01073-0

    2024 doi

  7. [15]

    P., Raza, M

    Venkatesh, K. P., Raza, M. M., & Kvedar, J. C. (2022). Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00694-7

    2022 doi

  8. [16]

    Bian, J., Huang, Y., Dai, H., Xu, J., Wei, R., Sun, L., Guo, Y., & Guo, J. (2024). Evolution of digital twins in precision health applications: a scoping review study. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs- 4612942/v1

    2024 doi

  9. [17]

    & Ding, Xd

    Shen, Md., Chen, Sb. & Ding, Xd. The effectiveness of digital twins in promoting precision health across the entire population: a systematic review. npj Digit. Med. 7, 145 (2024). https://doi.org/10.1038/s41746-024-01146-0

    2024 doi

  10. [18]

    Sun, T., He, X., Song, X., Shu, L., & Li, Z. (2022). The Digital Twin in Medicine: A Key to the Future of Healthcare?. Frontiers in medicine, 9, 907066. https://doi.org/10.3389/fmed.2022.907066

    2022

  11. [19]

    F., Groth, P., & Degeler, V

    Karabulut, E., Pileggi, S. F., Groth, P., & Degeler, V. (2023). Ontologies in digital twins: A systematic literature review. Future Generation Computer Systems, 153, 442–456. https://doi.org/10.1016/j.future.2023.12.013

    2023 doi

  12. [20]

    Wilson, F., Hurley, R., Maxwell, D., McLellan, J., & Beverley, J. (2024). Foundations for digital twins. arXiv.org. https://arxiv.org/abs/2405.00960

    2024 arXiv

  13. [21]

    Petrova-Antonova, D., & Ilieva, S. (2020). Digital twin modeling of smart cities. In Advances in intelligent systems and computing (pp. 384– 390). https://doi.org/10.1007/978-3-030-55307-4_58 39

    2020 doi

  14. [22]

    Enabling technologies and tools for digital twin

    Qi, Q., Tao, F., Hu, T., Anwer, N.n Liu, A., Wei, Y., Wang, L., Nee, A. Enabling technologies and tools for digital twin. (2021). Journal of Manufacturing Systems 58(Part B). 3-21. https://doi.org/10.1016/j.jmsy.2019.10.001

    2021 doi

  15. [23]

    McClellan, A., Lorenzetti, J., Pavone, M., & Farhat, C. (2022). A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 380(2229)....

    2022

  16. [24]

    Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130. https://doi.org/10.1016/j.compind.2019.103130

    2019

  17. [25]

    Kapteyn, M., Knezevic, D., Huynh, D., Tran, M., & Willcox, K. (2020). Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models. International Journal for Numerical Methods in Engineering, 123(13), 2986–

    2020

  18. [26]

    Ding, X., & Xing, J. (2025). Modeling and implementation of a real-time digital twin for the Stewart platform with real-time trajectory computation. PeerJ Computer Science, 11, e2892. https://doi.org/10.7717/peerj-cs.2892

    2025 doi

  19. [27]

    & Vallecillo, A

    Deantoni, J., Muñoz, P., Gomes, C., Verbrugge, C., Mittal, R., Heinrich, R., Bellis, S. & Vallecillo, A. (2025). Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems. at - Automatisierungstechnik, 73(2), 81-

    2025

  20. [28]

    J., Worden, K., Barthorpe, R

    Wagg, D. J., Worden, K., Barthorpe, R. J., & Gardner, P. (2020). Digital Twins: State-of-the-Art and future Directions for modeling and simulation in Engineering Dynamics Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineeri...

    2020 doi

  21. [29]

    Towards a taxonomy of digital twin evolution for technical sustainability

    David, I., Bork, D. Towards a taxonomy of digital twin evolution for technical sustainability. (2023). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/document/10350387

    2023

  22. [30]

    Blair, G. (2025). Digital twins of the natural environment. Patterns 2(10). https://doi.org/10.1016/j.patter.2021.100359

    2025

  23. [31]

    Hazeleger, W., Aerts, J. P. M., Bauer, P., Bierkens, M. F. P., Camps-Valls, G., Dekker, M. M., Doblas-Reyes, F. J., Eyring, V., Finkenauer, C., Grundner, A., Hachinger, S., Hall, D. M., Hartmann, T., Iglesias-Suarez, F., Janssens, M., Jones, E. R., Kölling, T., Lees, M., Lherm...

    2024 doi

  24. [33]

    S., De Souza Araújo, T., Rohrich, R

    Cardoso, F. S., De Souza Araújo, T., Rohrich, R. F., & De Oliveira, A. S. (2025). Modeling, evaluation and metrics performance of the SyncLMKD in distributed kinematics variations. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-024- 84997-7

    2025 doi

  25. [34]

    Shao, G. (2024). Manufacturing Digital Twin Standards. Association for Computing Machinery, MODELS Companion '24: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. 370–377. https://doi.org/10.1145/3652620.3688250

    2024

  26. [35]

    National Institute of Standards and Technology

    Drobnjakovic, M., Shao, G., Nikolov, A., Kulvatunyou, B., Frechette, S., Srinivasan., V. National Institute of Standards and Technology. (2023). Towards ontologizing a digital twin framework for manufacturing. (NIST Internal Report 8356). https://tsapps.nist.gov/publication/ge...

    2023

  27. [36]

    Review of digital twin about concepts, technologies, and industrial applications

    Liu, M., Fang, S., Dong, H., & Xu, C. Review of digital twin about concepts, technologies, and industrial applications. (2021). Journal of Manufacturing Systems 58(Part B). https://doi.org/10.1016/j.jmsy.2020.06.017

    2021 doi

  28. [37]

    Towards a Framework for the Classification of Digital Twins and their Applications

    Pronost, G., Mayer, F., Marche, B., Camargo, & M., Dupont, L. Towards a Framework for the Classification of Digital Twins and their Applications. (2021). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/9570114

    2021

  29. [38]

    Flammini, F. (2021). Digital twins as run-time predictive models for the resilience of cyber-physical systems: a conceptual framework. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 379(2207), 20200369. https://doi.org/10.1098...

    2021

  30. [39]

    Dai, C., Cheng, K., Liang, B., Zhang, X., Liu, Q., & Kuang, Z. (2024). Digital twin modeling method based on IFC standards for building construction processes. Frontiers in Energy Research, 12. https://doi.org/10.3389/fenrg.2024.1334192

    2024

  31. [40]

    Luo, F., Feng, S., Yang, Y., Ao, Z., Li, X., & Chai, Y. (2022). Ontology modeling method applied in simulation modeling of distribution network time series operation. Frontiers in Energy Research, 10. https://doi.org/10.3389/fenrg.2022.935026

    2022

  32. [41]

    Bitencourt, J., Wooley, A., & Harris, G. (2024). Verification and validation of digital twins: a systematic literature review for manufacturing applications. International 41 Journal of Production Research, 1–

    2024

  33. [42]

    Ammar, M., Mousavi, A., & Al-Raweshidy, H. (2024). Physics Based Digital Twin Modelling from Theory to Concept Implementation Using Coiled Springs Used in Suspension Systems. Tech Science Press. https://doi.org/10.32604/dedt.2023.044930

    2024

  34. [44]

    https://doi.org/10.1080/00207543.2024.2357741

    2024

  35. [99]

    https://doi.org/10.1515/auto-2024-0036

    2024 doi

  36. [3003]

    https://doi.org/10.1002/nme.6423

    doi

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