Simulating Pressure And Velocity Time Series With Artificial Neural Networks: Some Advantages And Pitfalls
Bibliographic record
Abstract
Three examples of time series simulations of pressure and velocity fluctuations using artificial neural networks were discussed: (i) a spatial interpolation of pressure time series on the roof of a low building in a thick, turbulent boundary layer, (ii) a simulation of two velocity components at multiple spatial locations simultaneously in the turbulent far wake of a circular cylinder, and (iii) a simulation of pressure time series around the surface of a circular cylinder in a crossflow. For the spatial interpolation a backpropagation network was used, while for the other two simulations, the fuzzy ARTMAP neural classifier was used. It was shown that the fuzzy ARTMAP captured the energy of the fluctuations over a wider range of scales than the backpropagation network because of its architecture, even though the input and output types were similar. The fuzzy ARTMAP is based on a clustering-type of pattern recognition while the backpropagation network is more deterministic, i.e., more like an empirical curve-fit to the data. This appears to allow the fuzzy ARTMAP to capture the dynamics of the flow field to a greater extent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".