Unsupervised Residual Vector Analysis for Mesh Optimization
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0833.vid This work prototypes novel methods to enhance the efficiency of a previous CFD stability improvement approach. The feasibility of residual vector analysis for unstable solution mode identification is studied. Unsupervised machine learning models in the form of outlier detectors are used to identify anomalous vector elements. A novel method is presented to construct synthetic vectors that resemble unstable eigenvectors. Synthetic vectors are used to find vertices for modification and calculate the vertex movement direction and magnitude. The residual vector helps substantially in reducing the computational cost of the optimization algorithm. In comparison to the full eigenanalysis of the Jacobian matrix, residual vector analysis requires much fewer computational resources. This methodology is used for the first time for the stability improvement of finite-volume simulations. It is shown that using residual vector analysis for the identification of the unstable solution modes and problematic cells in the mesh has a similar stabilization performance to using the right eigenvectors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".