Continuous Evolution of Digital Twins using the DarTwin Notation
Bibliographic record
Abstract
Abstract Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user’s needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimisation of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user’s requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop , a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home’s DT, whose purpose is expanded, changed and re-prioritised throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a laboratory-scale gantry crane’s DT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".