Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics. (arXiv:2206.02563v1 [stat.ML])

This paper introduces algorithms to select/design kernels in Gaussian process
regression/kriging surrogate modeling techniques. We adopt the setting of
kernel method solutions in ad hoc functional spaces, namely Reproducing Kernel
Hilbert Spaces (RKHS), to solve the problem of approximating a regular target
function given observations of it, i.e. supervised learning. A first class of
algorithms is kernel flow, which was introduced in a context of classification
in machine learning. It can be seen as a nested cross-validation procedure
whereby a …

aerodynamics application arxiv data learning ml process regression