Generation of functional mock-up units for co-simulation from simulink®, using explicit computational semantics: work in progress paper
Bibliographic record
Abstract
As the complexity of Software-Intensive and Cyber-Physical Systems increases, multiple formalisms are used to model different parts of a system. Rather than building simulators for these combinations of multiple formalisms, co-simulation is often used to orchestrate multiple simulations. One emerging industry standard in this field is the Functional Mock-up Interface (FMI). This standard defines the interface implemented by Functional Mock-up Units (FMUs). An FMU is encoded as a zip-file containing model variable types and values in XML-format as well as the model's equations in C C-code. The C encoding allows one to distribute IP in binary form. Solvers are typically coded instead of explicitly modeled. However, this does not allow straightforward analysis or detection of for example algebraic loops and optimization possibilities. Explicitly modeling the solvers helps overcome these limitations, since this allows for the use of model-driven engineering techniques, such as model transformations. This paper presents a method to generate FMUs from Causal Block Diagram models, more specific Simulink® models, with explicitly modeled ODE solvers. The execution performance is compared between FMUs with explicitly modeled solvers and FMUs with coded solvers. We conclude that modeling the solver has a significant positive impact on the run-time efficiency of the generated FMUs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".