Error Estimation

Error estimation focuses on quantifying the uncertainty associated with predictions or estimations from various models, aiming to provide reliable confidence intervals and identify sources of inaccuracy. Current research emphasizes data-driven approaches, employing neural networks (including physics-informed neural networks and convolutional neural networks), reinforcement learning, and resampling methods like bootstrapping to estimate errors across diverse applications, from solving partial differential equations to analyzing large language models. Accurate error estimation is crucial for improving model reliability and robustness, impacting fields ranging from scientific computing and machine learning to safety-critical applications like human-robot interaction and industrial automation.