Spatial Convolution Neural Network for Efficient Prediction of Aerodynamic Coefficients
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.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".