Residual Error

Residual error, the difference between a model's prediction and the true value, is a central concern across diverse scientific fields, driving research aimed at accurate estimation and effective mitigation. Current efforts focus on developing efficient algorithms for residual error estimation in various contexts, including matrix approximations, neural network training (particularly in spiking neural networks and physics-informed neural networks), and robotic system modeling, often employing techniques like sketching, Kalman filtering, and deep learning. Understanding and controlling residual error is crucial for improving the accuracy and reliability of models in numerous applications, ranging from image processing and anomaly detection to solving partial differential equations and advancing robotics.