Radial Basis Function

Radial basis functions (RBFs) are mathematical functions used in various machine learning models to approximate complex, high-dimensional data. Current research focuses on enhancing RBF networks' performance and applicability through hybrid architectures combining RBFs with neural networks (e.g., Physics-Informed Neural Networks, or PINNs), and exploring their use in diverse applications such as time series imputation, anomaly detection, and solving partial differential equations. This versatility makes RBFs a powerful tool for tackling challenging problems across scientific computing, engineering, and other fields requiring accurate function approximation and data analysis.