README.md

metadata

library_name: pytorch
tags:
  - tabular
  - pytorch
  - classification
  - aviation
  - wing-design
datasets:
  - ecopus/transport-wings-500
license: mit

Wing Selector MLP

This repository contains a PyTorch MLP that scores aircraft-style wings within the same airfoil for a chosen objective:

• min_cd (minimize drag),
• max_cl (maximize lift),
• max_ld (maximize lift-to-drag).

It was trained on the dataset ecopus/transport-wings-500.

Files

• best.pt – best checkpoint by validation top-1@group  
• last.pt – final checkpoint after training  
• config.json – input dim, #airfoils, feature scaler stats  
• feature_names.json – expected feature order  
• airfoil_vocab.json – airfoil name → id mapping used during training  
• inference.py – minimal loader & scoring helper

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Model Architecture

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.

• Inputs: The model takes three inputs:
  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.
  2. Objective ID: A one-hot encoded vector representing one of the three possible design objectives.
  3. Airfoil ID: An embedding vector that learns a representation for each unique airfoil in the training data.
• Embedding Layer: An nn.Embedding layer converts the discrete airfoil ID into a dense 8-dimensional vector.
• Hidden Layers: The core of the network consists of two fully connected hidden layers, each with 128 neurons and using the ReLU activation function.
• Output Layer: A final linear layer outputs a single logit, which represents the model's prediction score.

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Training Hyperparameters

Hyperparameter	Value
Epochs	50
Batch Size	64
Learning Rate	2e-3
Optimizer	AdamW
LR Scheduler	CosineAnnealingLR
Loss Function	BCEWithLogitsLoss
Seed	42

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Final Training Metrics

Metric	Value
Validation AUC	0.9790
Validation Avg. Precision	0.638
Validation Top-1 Accuracy	50.0%