Model Updating and Data Assimilation in Engineering Digital Twins: A Cross-Domain Critical Review of Methods, Evidence, and Validation
- Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, Psachna, Greece
- Institute of Structural Analysis and Antiseismic Research, Department of Structural Engineering, School of Civil Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Zografou Campus, GR-15780 Athens, Greece
Corresponding author: Stelios K. Georgantzinos, sgeor@uoa.gr.
Keywords:
Digital Twin, model updating, Bayesian calibration, uncertainty quantification, validation and credibility, engineering systems, data assimilation, state and parameter estimationAbstract
Engineering Digital Twins rely on the continuous alignment of virtual models with evolving physical systems, yet the computational mechanisms that enable this alignment are often described inconsistently and assessed with uneven standards of evidence. This review critically examines model updating and data assimilation as the principal physical-to-virtual synchronization mechanisms of engineering Digital Twins across structural, manufacturing, aerospace, thermal, energy, robotic, and built-environment applications. Particular attention is given to the quantities being updated, including dynamic states, physical parameters, unknown inputs, boundary conditions, model structure, and model-form discrepancy, as well as to the temporal regime in which updating is performed. The synthesis shows that no inference method is universally preferable. Deterministic and finite-element updating provide transparent solutions for interpretable batch calibration; Bayesian approaches support uncertainty-aware parameter and discrepancy estimation; Kalman and particle-filter methods enable sequential state and parameter assimilation; and reduced-order, surrogate, and hybrid models address the computational demands of operational deployment. Method suitability depends on identifiability, model fidelity, nonlinearity, observation cadence, computational cost, uncertainty structure, interpretability requirements, and the consequence of prediction error. Across application domains, the most credible studies combine a complete physical-observation loop with independent or changed-condition validation, explicit uncertainty treatment, defensible latency assessment, and clearly defined applicability limits. Recurring weaknesses include conflation of calibration with validation, unsupported real-time claims, incomplete treatment of model-form error, and limited reproducibility. The review develops a unified taxonomy, an evidence-based method-selection framework, a validation-maturity hierarchy, and reporting guidance for the development of credible, continuously evaluated, and decision-relevant engineering Digital Twins.
Declarations
- Author contributions
- S.K.G.: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing—original draft, and Writing—review and editing. N.D.L.: Methodology, Validation, Supervision, and Writing—review and editing.
- Data availability
- Data are available from the corresponding author upon reasonable request.
- Declaration of generative AI assistance
- Generative AI tools were used to support language editing, document structuring, visual preparation, and bibliographic workflow management. The authors remain responsible for source verification, scientific interpretation, corpus decisions, appraisal scores, and the final manuscript.
- Ethics approval and consent
- Not applicable. This review synthesized published literature and did not involve human participants, animals, clinical interventions, or identifiable personal data.
- Funding
- This research received no external funding.
- Competing interests
- S.K.G. and N.D.L. are editors of IJDTDE.
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