#physics-informed-neural-networks2 articles

2 articles

AIBullisharXiv – CS AI · 6h ago2

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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.

AINeutralarXiv – CS AI · 6h ago0

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Solving Inverse PDE Problems using Minimization Methods and AI

Researchers published a study comparing traditional numerical methods with Physics-Informed Neural Networks (PINNs) for solving direct and inverse problems in differential equations. The work demonstrates that PINNs can effectively estimate solutions at competitive computational costs for complex systems like the Porous Medium Equation.