Fourier Neural

Fourier neural networks leverage the Fourier transform to represent and process data, primarily focusing on improving the efficiency and accuracy of neural network models for various tasks, particularly time-series analysis and solving partial differential equations. Current research emphasizes developing novel architectures like Fourier Neural Operators and incorporating physics-informed constraints within these frameworks, often employing techniques such as adaptive basis selection and strong boundary conditions to enhance performance and generalization. This approach offers significant advantages in terms of computational efficiency, improved handling of high-frequency information, and enhanced interpretability, impacting fields ranging from weather forecasting and geological modeling to signal processing and medical imaging.