Adaptive Sampling For Building Simulation Surrogate Model Derivation Using The LOLA-Voronoi Algorithm
Bibliographic record
Abstract
Statistical surrogate models, or meta-models, are used to emulate building simulation models. Their key advantage is the reduction of computational cost. This in particular matters if building design analysis demands to explore a large number of different building designs options as in optimization or uncertainty analysis problems. To derive a surrogate model, a data set consisting of simulation in- and output data is generated. This set is then used to train the surrogate. This process of collecting simulation data may be time intensive and a building designer has to wait until surrogate model is available. In this study we construct a global surrogate model using adaptive sampling to speed up the data collection. In comparison to static sampling, it balances both exploration of the design space while exploiting the iteratively growing information of simulation outcomes. The advantage of adaptive sampling is not only that it can cut simulation time, but also that it rapidly provides a preliminary low-accurate surrogate to the building designer which is sequentially improved while he/she is working with the low accuracy model already.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".