Spatial Convolution Neural Network for Efficient Prediction of Aerodynamic Coefficients
Why this work is in the frame
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0277.vid Deep learning has recently been applied to predict aerodynamic coefficients using a Convolutional Neural Network (CNN) architecture over an image representation of an airfoil. We introduce a novel architecture, the Element Spatial Convolutional Neural Network (ESCNN) to improve on the image processing approach. Instead of processing the airfoils as images, the ESCNN directly takes airfoil coordinates as input and output aerodynamic coefficients, which enables end to end training and prediction. Compared with other CNNs, ESCNN is orders of magnitude smaller in terms of parameters, while still reaching state of the art prediction accuracy. The model prediction capacity is validated on a dataset that contains a large number of airfoil shapes and their aerodynamic coefficients. In addition to prediction, ESCNN can be used to perform airfoil optimization.The computational efficiency of ESCNN makes it possible to achieve real time prediction on embedding systems with constrained memory and limited computing power.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it