UncertaintyQuantifizierung

UncertaintyQuantifizierung, often abbreviated as UQ, is the field dedicated to the mathematical treatment and assessment of the uncertainty that arises in computational modeling and data analysis. It acknowledges that model predictions and data interpretations are rarely exact and instead are subject to various sources of error and variability.

The primary goal of UQ is to identify, characterize, and quantify these uncertainties. This involves understanding where the uncertainty originates, such as from uncertain input parameters, imprecise model structures, or limitations in the available data. Once identified, UQ employs a range of statistical and computational techniques to estimate the extent of this uncertainty.

Key methods within UQ include sensitivity analysis, which determines how much each source of uncertainty contributes to the overall uncertainty in the output. Probabilistic approaches, such as Monte Carlo simulations, are widely used to propagate input uncertainties through a model to generate a distribution of possible outcomes. Bayesian inference is another powerful tool, allowing for the updating of beliefs about model parameters and predictions as new data becomes available.

The results of UQ are crucial for informed decision-making. By understanding the range of possible outcomes and the confidence in predictions, stakeholders can make more robust and reliable choices, especially in fields like engineering, climate science, finance, and medicine, where consequences of erroneous predictions can be significant. UQ helps to move beyond point estimates to a more complete picture of model behavior and its limitations.