An Assessment Framework for the Maturity of Simulation-Based Verification
Bibliographic record
Abstract
Simulation-based verification is a term that promises to reduce lengthy and costly ground and flight tests of aircraft, using high-fidelity physics-based simulations instead of testing physical assets. There has been considerable research into building more robust aircraft models that accurately depict the physics of an operational aircraft. The focus has been on the 'simulation-based' portion of the phrase 'simulation-based verification.' However, to achieve the full benefit of simulation-based verification, the focus needs to include how these physics-based simulations can be incorporated into an organization's requirements verification activities, which are traditionally the responsibility of the systems engineering organization. Over the last decade, systems engineering has been transforming into a 'model-based' discipline, where all systems engineering data is contained in a model of the system, including requirements and their properties. With a drive toward simulation-based verification, there is a need to incorporate the results of simulation-based verification into the system architecture model that contains the system's requirements. By integrating physics-based simulation into the system architecture models, organizations can build a robust verification story and to automate the requirements verification process. However, an organization does not simply transform from a traditional, physical test-based organization into a simulation-based organization; it is an evolutionary process. This paper describes a framework for assessing the maturity of an organization's simulation-based verification capabilities, with the highest level of maturity having physics-based simulation integrated into the model-based systems engineering verification process.
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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 | 0.002 | 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".