Uncertainty Visualization

Uncertainty visualization focuses on effectively communicating the inherent uncertainty present in data and model predictions, aiming to improve decision-making and enhance the trustworthiness of scientific findings. Current research emphasizes developing novel visualization techniques for various data types (e.g., time series, vector fields, images, and high-dimensional data) and incorporating uncertainty estimation methods within deep learning models (such as deep ensembles and Monte Carlo dropout). These advancements are crucial for diverse fields, improving the interpretability of complex models and enabling more informed analyses in applications ranging from medical diagnosis to climate modeling.