Uncertainty Information

Uncertainty information, encompassing the quantification and propagation of uncertainty in data and models, is a rapidly growing field aiming to improve the reliability and trustworthiness of scientific inferences and technological applications. Current research focuses on incorporating uncertainty into various models, including machine learning algorithms (like Gaussian Processes, Mixture Density Networks, and graph neural networks), and developing methods for uncertainty quantification and propagation across diverse domains, from natural gas simulations to medical image analysis. This work is crucial for enhancing decision-making in high-stakes applications and improving the robustness and interpretability of complex models, ultimately leading to more reliable scientific findings and technological advancements.