Learning the Hughes Model Using Neural Operators

This group extends neural operator methods to solve the Hughes model for pedestrian flow, a computationally challenging PDE system. Their goal is to develop efficient models that simulate evacuation and crowd movement in complex spaces using Fourier-based architectures and convolutional kernels. Applications include urban safety and traffic flow optimization.