A Relative Adequacy Framework for Multimodel Management in Multidisciplinary Design Optimization
Bibliographic record
Abstract
In previous work, we presented a novel relative adequacy framework to manage the employment of a set of available computational models in (single-disciplinary) design optimization problems. In this paper, we extend our method to solve multidisciplinary design optimization problems with particular emphasis on strongly coupled fluid-structure interactions. We illustrate that these interactions can have a significant impact on multimodel management: models that may be selected in a single-disciplinary analysis context can be inadequate in a multidisciplinary analysis one. We implement our method for two multidisciplinary design optimization architectures: the monolithic multidisciplinary feasible formulation and a penalty-based distributed interdisciplinary feasible formulation. We illustrate the proposed multimodel management methodology by means of two example problems: a flexible beam fluid-structure interaction problem and a transonic fan flow problem. The obtained results demonstrate that our framework is accurate and efficient while exhibiting significant computational cost benefits, especially when disciplinary coupling is tight.
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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 | 0.009 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".