Error Model

Error modeling focuses on understanding and quantifying inaccuracies in various systems, aiming to improve prediction accuracy and system robustness. Current research emphasizes developing data-driven models, such as neural networks and Gaussian processes, to capture complex error patterns, particularly in areas like language processing, autonomous systems, and complex dynamical systems. These advancements are crucial for enhancing the reliability and performance of AI systems, improving the accuracy of scientific models, and enabling more effective error correction and compensation techniques in diverse applications. The development of robust error models is vital for building trustworthy and reliable systems across numerous scientific and engineering domains.