System Uncertainty

System uncertainty, encompassing inaccuracies and variations in models and data, is a critical challenge across diverse scientific and engineering domains. Current research focuses on developing methods to quantify and mitigate this uncertainty, employing techniques like sensitivity analysis, Bayesian deep learning, and distributionally robust optimization within various model architectures, including LiDAR-inertial odometry and SINDy algorithms. Addressing system uncertainty is crucial for improving the reliability and safety of autonomous systems, enhancing the accuracy of scientific predictions, and enabling more efficient data-driven decision-making in fields ranging from aerospace engineering to robotics. The development of robust and computationally efficient uncertainty quantification and mitigation strategies is a major focus of ongoing research.