An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems
Bibliographic record
Abstract
We introduce an algorithm for the optimization of problems whose objective functions are evaluated by computationally expensive black-box simulations and for which an analytic description of the objective and its derivatives are not available. We consider the case where two levels of simulation model fidelity are available, namely a high fidelity model that is computationally very expensive to evaluate, and a low fidelity model that is less accurate and computationally cheaper but still time consuming. The computational effort is alleviated by using computationally cheap surrogate models that approximate the simulations at both fidelity levels. The local correlation between both fidelity surrogate models determines when the low fidelity model can be trusted for making sampling decisions for the high fidelity model. In the numerical experiments we investigate how well our algorithm responds to problems whose objective function fidelity levels are well correlated and badly correlated. We study how different initial design strategies and parameter settings impact the performance of the algorithm. The results show that our algorithm actively learns from the local correlation computations how well suited the low fidelity model is for making sampling decisions and it ignores the low fidelity model if the correlation is too low.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".