  Gaussian Process (GP) regression is a flexible non-parametric approach to
approximate complex models. In many cases, these models correspond to processes
with bounded physical properties. Standard GP regression typically results in a
proxy model which is unbounded for all temporal or spacial points, and thus
leaves the possibility of taking on infeasible values. We propose an approach
to enforce the physical constraints in a probabilistic way under the GP
regression framework. In addition, this new approach reduces the variance in
the resulting GP model.
