Pseudo-differential-enhanced physics-informed neural networks
This method enhances physics-informed neural networks by applying gradient enhancement techniques in Fourier space to improve training efficiency for solving partial differential equations. It uses polynomial representations of differential operators in Fourier space to better incorporate high-frequency PDE information into the loss function, leading to faster learning and more stable results.