  We show that Gaussian process regression (GPR) allows representing
multivariate functions with low-dimensional terms via kernel design. When using
a kernel built with HDMR (High-dimensional model representation), one obtains a
similar type of representation as the previously proposed HDMR-GPR scheme while
being faster and simpler to use. We tested the approach on cases where highly
accurate machine learning is required from sparse data by fitting potential
energy surfaces and kinetic energy densities.
