Multi-level design optimization considering uncertainties in configurations and parameters
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 this research, a new approach is introduced for multi-level design optimization considering both parameter uncertainties and configuration uncertainties. In this work, an AND-OR tree is used to represent the generic design based on requirements. Nodes of the AND-OR tree are used to model partial design configuration solutions including the solutions considering possible configuration changes in the future. A node is further defined by parameters and their variations due to uncertainties. Design configuration candidates and their possible configuration changes are created from the AND-OR tree through a tree-based search. Each configuration candidate is defined by the parameters of the nodes and variations of these parameters. The optimal design configuration and its parameter values are achieved by a two-level optimization method. Parameter optimization is conducted for each design configuration candidate, while configuration optimization is conducted to obtain the best design configuration. Both the objective function and variation of the objective function due to uncertainties in configurations and parameters are considered in the multi-level 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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