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Fast Dynamics with Graph Recurrent Neural Nets

Test Case Showcase

This work applies our advanced graph neural network architecture to predict aerodynamic loads on aircraft surfaces. 

Without need for solving traditional costly fluid dynamics equations. 

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This test case was essential to cover:

Key Highlights

  • Built on Graph Convolutional Networks (GCNs) tailored for unstructured CFD meshes 

  • Uses a multi-resolution autoencoder to compress and reconstruct aerodynamic fields 

  • Tested on the NASA Common Research Model wing/body configuration 

  • Predicts pressure and shear-stress coefficients across nonlinear transonic regimes 

Impact

  • Drastically reduced computational cost compared to traditional CFD 

  • Enables fast, scalable aerodynamic analysis for design and optimization 

  • Opens new possibilities for real-time simulation and control in aerospace engineering 

Benefits Summary

  • Lightweight: while other physics AI approaches require top-spec high-memory GPUs, our lightweight implementation runs on your laptop, with a GPU memory as lows as 6 GB. 

  • Efficiency: AI models learn complex flow patterns, reducing simulation time from hours to seconds 

  • Scalability: Easily adaptable to different aircraft geometries and flight conditions 

  • Insight: Reveals hidden aerodynamic relationships through learned representations 

  • Integration: Ideal for embedding into digital twins, design loops, and autonomous systems 

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