G-PINNs: Gaussian-based spatially weighted formulation for PINNs: 1D low-viscous Burgers

Kheir-eddine Otmani, Abdelhalim Azzouz, Nourelhouda Groun +1

We introduce a Gaussian-based spatially weighted loss framework (G-PINNs) for physics-informed neural networks (PINNs) to improve the resolution of sharp discontinuities and shock…