Error Analysis

Error analysis focuses on identifying and quantifying inaccuracies in models and algorithms across diverse fields, aiming to improve their reliability and performance. Current research emphasizes developing methods for systematic error analysis, often employing neural networks (including various CNN and Transformer architectures) and advanced algorithms like projected gradient descent, to pinpoint error sources in applications ranging from PDE solving and reinforcement learning to natural language processing and image recognition. These advancements are crucial for enhancing the trustworthiness and practical utility of machine learning models in various scientific and industrial domains, leading to more robust and reliable systems.