A similarity-assisted multi-fidelity approach to conceptual design space exploration
Why this work is in the frame
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Bibliographic record
Abstract
In conceptual design studies engineers typically utilize data-based surrogate models to enable rapid evaluation of design objectives that otherwise would be too computationally expensive and time-consuming to simulate. Due to the computationally expensive simulations, the data-based surrogate models are often trained using small sample sizes, resulting in low-fidelity models which can produce results that are not trustworthy. To mitigate this issue, a similarity-assisted design space exploration method is proposed. The similarity is measured between design points that have been evaluated through lower-fidelity data-based surrogate models and design points that have been evaluated using higher-fidelity physics-based simulations. This similarity information can then be used by design engineers to better understand the trustworthiness of the data produced by the low-fidelity surrogate models. Our numerical experiments demonstrate that such a similarity measurement can be used as an indicator of the trustworthiness of the lower-fidelity model predictions. Moreover, a second similarity metric is proposed for measuring the similarity of new designs to legacy designs, thus highlighting the potential to reuse knowledge, analysis models, and data. The proposed method is demonstrated by means of an aero-engine structural component conceptual design study. An open-source software tool developed to assist in data visualization is also presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it