Designing a Reusable Pipeline Architecture for Cross-Domain Simulations
Bibliographic record
Abstract
Simulation and digital twin (DT) technologies are powerful tools for analyzing complex systems, but their creation is often slow, costly and highly dependent on expert knowledge. Current methods provide strong support for the design and execution phases, yet very little attention has been given to automatically linking the two. As a result, most simulations are developed as one-off solutions that are difficult to reuse across domains. This paper addresses this gap by proposing a reusable and modular pipeline architecture for automated simulation generation. The approach defines a step-by-step workflow that begins with abstraction and conceptual modeling, passes through formal specification and validation, and ends with deployment in cloud-based environments. To support the relevance of this work, the paper also analyzes ten highly cited review articles on simulation and DT architectures published between 2020 and 2025, showing that automated generation is the least developed stage in the lifecycle. By combining semantic modeling, process mining, and cloud deployment strategies, the proposed architecture lowers the barrier to simulation development and provides a pathway toward scalable, cross-domain, and “digital twin-ready” solutions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".