Error Bound

Error bounds research focuses on rigorously quantifying the uncertainty and accuracy of predictions made by various models, particularly in machine learning and scientific computing. Current research emphasizes developing tighter error bounds for diverse model architectures, including neural networks (e.g., deep kernel learning, physics-informed neural networks), Gaussian processes, and interpolation methods, often within specific application domains like safety-critical systems or PDE solving. These advancements are crucial for building trust in model predictions, enabling reliable decision-making in high-stakes applications and improving the overall robustness and verifiability of scientific machine learning workflows. The development of data-driven error estimation techniques is also a significant area of focus, aiming to provide more practical and less conservative bounds than traditional theoretical approaches.