Predicting Lateral Dynamics of CRJ700 Using Multilayer Perceptron and Support Vector Regression
Why this work is in the frame
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Bibliographic record
Abstract
Accurate estimation of lateral aerodynamic coefficients is essential for improving flight stability and control. This study explores machine learning techniques, specifically multilayer perceptron (MLP) and support vector regression (SVR), to predict the lateral aerodynamic coefficients of a Bombardier Regional Jet CRJ-700. The dataset, obtained from a Level D CRJ-700 Virtual Research Simulator (VRESIM), covers diverse flight conditions. Bayesian optimization was used for hyperparameter tuning. Model performance was validated by comparing predictions with experimental data within FAA tolerance limits. The results show that MLP and SVR achieve lateral prediction errors below 5%, demonstrating high accuracy in estimating lateral aerodynamic coefficients. These findings suggest that AI-based methods can provide reliable aerodynamic models for flight simulation and control system design, reducing reliance on traditional empirical methods.
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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 | 0.002 |
| 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