Data-Free PINNs for Compressible Flows: Mitigating Spectral Bias and Gradient Pathologies via Mach-Guided Scaling and Hybrid Convolutions

Researchers developed a data-free Physics-Informed Neural Network (PINN) that can solve compressible flows around circular cylinders at extreme speeds up to Mach 15. The system uses hybrid convolutions and Mach-guided scaling to overcome traditional limitations and successfully captures shock waves without requiring training data.