The graphical depiction of uncertainty information is emerging as a problem of great importance in the ﬁeld of visualization. Scientiﬁc data sets are not considered complete without indications of error, accuracy, or levels of conﬁdence, and this information is often presented as charts and tables alongside visual representations of the data. Uncertainty measures are often excluded from explicit representation within data visualizations because the increased visual complexity incurred can cause clutter, obscure the data display, and may lead to erroneous conclusions or false predictions. However, uncertainty is an essential component of the data, and its display must be integrated in order for a visualization to be considered a true representation of the data. The growing need for the addition of qualitative information into the visual representation of data, and the challenges associated with that need, command fundamental research on the visualization of uncertainty.
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. UQ is key for achieving validated predictive computations in a wide range of scientific and engineering applications. The field relies on a broad range of mathematics and statistics groundwork, with associated algorithmic and computational development.