A New Approach to Multidisciplinary Design Optimization via Internal Decomposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional approaches to MDO problem decomposition have shown poor performance when solving problems with strong interactions between disciplines. We present a new decomposition strategy for MDO that aims to overcome this diculty. The method, called Block Approximation with Krylov Renement, or BAKR, decomposes the solution of the linear system present at each iteration of an interior point algorithm. By decomposing the linear system inside the optimization algorithm, rather than the original design problem, we maintain the strong global and local convergence properties of the interior point algorithm while reducing the overall computational cost of the solution. Preliminary test results show reductions in both computational cost and the number of function evaluations, demonstrating strong potential for future application in large MDO problems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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