Uncertainty Quantification

Characterizing uncertainty in computational predictions is essential to applications ranging from engineering design to climate modeling. To this end, uncertainty quantification (UQ) encompasses many tasks, including uncertainty propagation, sensitivity analysis, statistical inference and model calibration, decision making under uncertainty, optimal experimental design, and model validation. CCSE researchers are advancing the methodology and practice of UQ, drawing upon foundational ideas and techniques in applied mathematics and statistics (e.g., approximation theory, error estimation, stochastic modeling, and Monte Carlo methods) and focusing these techniques on mathematical and statistical models that are primarily accessible through computational simulation.