Fourier Feature

Fourier features leverage the power of Fourier transforms to represent data in the frequency domain, enabling efficient modeling of periodic and high-frequency patterns often missed by traditional neural networks. Current research focuses on incorporating Fourier features into various architectures, including Physics-Informed Neural Networks (PINNs), implicit neural representations (INRs), and recurrent neural networks (RNNs), to improve accuracy, efficiency, and robustness in tasks ranging from image processing and time series forecasting to solving partial differential equations and robotic control. This approach offers significant advantages in handling complex signals and high-dimensional data, leading to improved performance and reduced computational costs across diverse scientific and engineering applications.