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--- |
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library_name: pytorch |
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tags: |
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- tabular |
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- pytorch |
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- classification |
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- aviation |
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- wing-design |
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datasets: |
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- ecopus/transport-wings-500 |
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license: mit |
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--- |
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# Wing Selector MLP |
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This repository contains a PyTorch MLP that scores aircraft-style wings **within the same airfoil** for a chosen objective: |
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- **min_cd** (minimize drag), |
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- **max_cl** (maximize lift), |
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- **max_ld** (maximize lift-to-drag). |
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It was trained on the dataset **[ecopus/transport-wings-500](https://huggingface.co/datasets/ecopus/transport-wings-500)**. |
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## Files |
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- `best.pt` – best checkpoint by validation top-1@group |
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- `last.pt` – final checkpoint after training |
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- `config.json` – input dim, #airfoils, feature scaler stats |
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- `feature_names.json` – expected feature order |
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- `airfoil_vocab.json` – airfoil name → id mapping used during training |
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- `inference.py` – minimal loader & scoring helper |
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--- |
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### Model Architecture |
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The model is a feedforward neural network designed for a binary classification task. It predicts the "best" wing geometry for a given airfoil and aerodynamic objective. |
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* **Inputs**: The model takes three inputs: |
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1. **Wing Features**: A vector of 22 continuous features describing the wing's geometry and aerodynamic properties. These features are standardized (mean-centered and scaled by standard deviation) before being fed into the model. |
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2. **Objective ID**: A one-hot encoded vector representing one of the three possible design objectives. |
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3. **Airfoil ID**: An embedding vector that learns a representation for each unique airfoil in the training data. |
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* **Embedding Layer**: An `nn.Embedding` layer converts the discrete airfoil ID into a dense 8-dimensional vector. |
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* **Hidden Layers**: The core of the network consists of two fully connected hidden layers, each with **128 neurons** and using the **ReLU** activation function. |
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* **Output Layer**: A final linear layer outputs a single logit, which represents the model's prediction score. |
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--- |
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### Training Hyperparameters |
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| Hyperparameter | Value | |
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| :--- | :--- | |
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| **Epochs** | 50 | |
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| **Batch Size** | 64 | |
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| **Learning Rate** | `2e-3` | |
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| **Optimizer** | AdamW | |
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| **LR Scheduler** | CosineAnnealingLR | |
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| **Loss Function** | BCEWithLogitsLoss | |
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| **Seed** | 42 | |
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--- |
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### Final Training Metrics |
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| Metric | Value | |
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| :--- | :--- | |
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| **Validation AUC** | 0.9790 | |
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| **Validation Avg. Precision**| 0.638 | |
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| **Validation Top-1 Accuracy**| 50.0% | |
