Towards Rapid Redesign: Pattern-Based Redesign Planning for Large-Scale and Complex Redesign Problems
Bibliographic record
Abstract
We have developed a decomposition-based rapid redesign methodology for large, complex computational redesign problems. While the overall methodology consists of two general steps: diagnosis and repair, this paper focuses on the repair step in which decomposition patterns are utilized for redesign planning. Resulting from design diagnosis, a typical decomposition pattern solution to a given redesign problem indicates the portions of the design model necessary for re-computation as well as the interaction part within the model accountable for design change propagation. Following this, this paper suggests repair actions with an approach derived from an input pattern solution, to generate a redesign roadmap allowing for taking a shortcut in the redesign solution process while scheduling re-computing tasks. To do so, a complete collection of re-computation strategies able to handle all possible decomposition patterns for any given redesign problem is introduced, and a two-stage redesign planning approach from re-computation strategy selection to redesign roadmap generation is proposed. An example problem concerning the redesign of a relief valve is used for illustration and validation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".