5.5.1 An Overview of the SysML‐Modelica Transformation Specification
Bibliographic record
Abstract
Abstract This paper provides an overview of the formal transformation between the two complementary languages: OMG SysML™ and Modelica. SysML is a standardized general purpose graphical modeling language for capturing complex system descriptions in terms of their structure, behavior, properties, and requirements. Modelica is a standardized general purpose systems modeling language for analyzing the continuous and discrete time dynamics of complex systems in terms of differential algebraic equations. Integrating the descriptive power of SysML models with the analytic and computational power of Modelica models provides a capability that is significantly greater than provided by SysML or Modelica individually. A standardized bi‐directional transformation between the two modeling languages is being developed that will support implementations to transfer efficiently and automatically the modeling information between SysML and Modelica models without ambiguity. In addition to an overview of this bi‐directional transformation approach, the paper provides a simple example to clarify the transformation principles and to illustrate the important synergies resulting from the integration between these two languages.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".