Uncertainty Quantification

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference

Uncertainty propagation in flow through porous media problems is a challenging problem. This is due to the high-dimensionality of the random property fields, e.g. permeability and porosity, as well as the computational complexity of the models that …

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

Computer codes simulating physical systems usually have responses that consist of a set of distinct outputs (e.g., velocity and pressure) that evolve also in space and time and depend on many unknown input parameters (e.g., physical constants, …

Multi-output local Gaussian process regression: Applications to uncertainty quantification

We develop an efficient, Bayesian Uncertainty Quantification framework using a novel treed Gaussian process model. The tree is adaptively constructed using information conveyed by the observed data about the length scales of the underlying process. …