Engineering Analysis with Uncertainties and Complexities, Using Reasoning Approaches
Bibliographic record
Abstract
Conventional computation methods generally limit practicing engineers from using complex formulations or considering uncertainties in general. A method is needed that can be implemented regardless of the uncertainty or linearity of the design parameters and their constraints. Methods such as qualitative reasoning provide an effective and sound technique for solving complex and uncertain scenarios. Uncertainties in engineering designs can be formulated as variables in the application domain and processed by numerical constraint reasoning. This paper describes the theories and algorithms behind a software platform built upon numerical constraint reasoning for engineering applications. The capability of representing design parameters and outcomes in a 2D solution space provides a practical way for engineers to leverage their existing knowledge and experience. The software expresses the results of the analysis in variable ranges and diagrams showing a 2D design space. Qualitative reasoning can assist in the difficult process of making appropriate engineering assumptions and judgments when carrying out complicated analysis procedures. In addition, interval constraint analysis can be used to derive controlling parameters and design space, therefore giving engineers a good overall understanding of a problem when practical experience is not available.
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.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".