Understanding uncertainty quantification

Uncertainty is an inherent part of any prediction, as there are always factors that are unknown or difficult to measure. Predictions are typically made based on incomplete data or models that are unable to capture the full complexity of the real world. As a result, the predictions are subject to various sources of uncertainty, including randomness, bias, and model errors.

To mitigate the risks associated with uncertainty, it is essential to quantify it accurately. By quantifying uncertainty, we can estimate the range of possible outcomes and assess the degree of confidence we can have in our predictions. This information can be used to make informed decisions and to identify areas where further research or data collection is needed.

UQ is a field of study that helps us measure how much we don’t know when we make predictions. UQ tries to estimate the probability of outcomes even if some aspects of the system under study are not known...