  In the paradigm of computer experiments, the choice of an experimental design
is an important issue. When no information is available about the black-box
function to be approximated, an exploratory design have to be used. In this
context, two dispersion criteria are usually considered: the minimax and the
maximin ones. In the case of a hypercube domain, a standard strategy consists
of taking the maximin design within the class of Latin hypercube designs.
However, in a non hypercube context, it does not make sense to use the Latin
hypercube strategy. Moreover, whatever the design is, the black-box function is
typically approximated thanks to kernel interpolation. Here, we first provide a
theoretical justification to the maximin criterion with respect to kernel
interpolations. Then, we propose simulated annealing algorithms to determine
maximin designs in any bounded connected domain. We prove the convergence of
the different schemes.
