Goal-oriented adaptive sampling for projection-based reduced-order models
Bibliographic record
Abstract
Modern aircraft design involves a large number of design parameters from a multitude of disciplines. Obtaining high-fidelity solutions for all combinations of such parameters is computationally unfeasible. Although the solution to a large-scale system of equations is generally an element of a large-dimensional space, the solution may actually lie on a reduced-order subspace induced by parameter variation. In order to capture this subspace, samples of the high-dimensional system called snapshots are used to build a reduced-order model. These models have generated interest as a means to compute high-fidelity solutions at a much lower computational cost. However, little value can be placed in a reduced-order solution without some quantification of its error. The dual-weighted residual can be used to obtain error estimates between the outputs of different models. Using dual-weighted residual error estimates in conjunction with a radial basis function interpolation, this work introduces a novel adaptive sampling method that chooses snapshots iteratively such that a prescribed output error tolerance is estimated to be met on the entirety of a parameter space. The adaptive sampling procedure is demonstrated on a one-dimensional Burgers’ equation and two-dimensional inviscid flows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".