Knowledge Assisted Optimization for Large-Scale Problems: A Review and Proposition
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
In engineering design, engineers often have certain knowledge about the design problem. However, in the last decades, we assume design functions are black-boxes. This paper discusses if knowledge can help with optimization, especially for large-scale optimization problems. Existing large-scale optimization methods based on black-box functions are first reviewed and the drawbacks of those methods are briefly discussed. To understand what knowledge is and what kinds of knowledge can be obtained and applied in design, the concepts of knowledge in both artificial intelligence (AI) and in the area of product design are reviewed. The relevant knowledge based engineering (KBE) system is also explained. Existing applications of knowledge in optimization, especially for large-scale optimization, are reviewed and categorized. Potential further applications of incorporating knowledge for optimization are discussed in more detail, in hope to identify possible directions for future research for knowledge assisted optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it