Optimization framework for multi-fidelity surrogate model based on adaptive addition strategy—A case study of self-excited oscillation cavity
Bibliographic record
Abstract
This study proposes a multi-fidelity efficient global optimization framework for the structural optimization of self-excited oscillation cavity. To construct a high-precision multi-fidelity surrogate model to correlate the structural parameters of a self-excited oscillation cavity with the gas precipitation and energy consumption characteristics by effectively fuzing the information of different fidelity levels, choosing different correlation functions and hyper-parameter estimation methods, and learning the correlation between the data. The optimization framework determines various sampling methods and quantities by calculating the minimum Euclidean distance between sample points and sensitivity index. To enhance computational efficiency, a multi-fidelity sample library is established by utilizing both precise and coarse computational fluid dynamics grids. The expected improvement criterion-based algorithm for global optimization is employed as an additive strategy to incorporate additional data points into the model. This approach considers both local and global search of the model, thereby enhancing sample accuracy while reducing computation time. Moreover, the utilization of the highly generalized Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for identifying the Pareto optimal solution set enhances convergence speed. The proposed optimization framework in this study achieves a remarkable level of model accuracy and provides optimal solutions even with a limited sample size. It can be widely used in engineering optimization problems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".