Uncertainty Quantification

Catalog description

Statistical and machine learning approaches to computational uncertainty quantification in mathematical models with applications to computer simulations, images, and time-series, spatio-temporal, and functional data. Topics include computer model emulation and design, reproducing kernel Hilbert spaces, Gaussian processes, dynamic systems, the Kalman filter, inverse problems, and Bayesian optimization.