SC071A

Submit Paper » Background Uncertainty Quantification (UQ) focuses on identifying, characterizing, and managing uncertainties in computational models and real-world systems. These uncertainties are classified into aleatory and epistemic types. Aleatory uncertainty, arising from inherent variability in natural systems, is irreducible, such as fluctuations in material properties. Epistemic uncertainty results from incomplete knowledge or assumptions in the modeling process and can be reduced with better information or models. Both types often coexist in practical problems, and quantifying them is essential for reliable predictions in various scientific and engineering disciplines. Forward Uncertainty Quantification (FUQ) is a specialized area within UQ that predicts how uncertain inputs affect model outputs, considering both aleatory and epistemic uncertainties. This process is vital for developing robust models that reflect real-world behavior under uncertainty. For example, FUQ helps ensure the safety and reliability of engineering structures by assessing how uncertainties influence their performance, aiding in decision-making, risk management, and optimization. ...