Gaussian Process
Hazem Yaghi (TU Braunschweig)
A Gaussian Process (GP) is a stochastic process over space or time
that, when restricted to a finite number of points, yields a collection
of jointly Gaussian random variables. It is considered as a
probabilistic supervised machine learning technique that can be applied
widely in regression and classification tasks.
In this talk we illustrate the surrogate modeling capabilities of GPs in
different scenarios. In the project SEMOTI we introduce a surrogate
modeling approach for a deep geological repository based on GPs. The
surrogate model is used as a substitute for the mechanical model in
many-query scenarios, such as parameter identification.
On other hand we apply GPs to find an approximate solution for a scalar
hyperbolic conservation law with uncertainty.