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A variety of experimental designs are incorporated into DOE/Opt . A nominal design generates a single run with nominal (default) values for all selected block inputs. A center point design is similar, but uses the midpoint value (between the maximum and minimum limits) for all selected inputs. Note that the range for input values may be specified directly, as deltas from the default value (e.g. ``+5''), or as percentages from the default value (e.g. ``-10%''). The axial design includes center points, and points at the minimum and maximum of each input while holding other inputs to their nominal values. The Box Wilson design includes the center point, and axial points and corner points distributed around an n-dimensional sphere circumscribing the cuboid defined by the input minima and maxima . We have found two variants of the conventional Box-Wilson design to be useful. The Box-Wilson (inscribed) modifies the design by distributing axial and corner points on an n-dimensional sphere inscribed within the cuboid . This has the benefit that no experimental points will be outside the input ranges, but results in models that are not well defined in the corners of cuboid. The Box-Wilson on a cube stretches the corner points to lie on the cuboid corners to achieve more complete coverage of the input parameter space. The full-factorial design produces a uniform grid with user-specified density covering the input parameter space. Other conventional designs, including Box-Behnken and half fractional, (as well as the half fraction complement for augmenting a screening design), are provided . Finally, Latin hypercube sampling (LHS)  provides an orthogonal array that randomly samples the entire design space broken down into equal-probability regions (where is the number of runs, and is the number of input variables). LHS can be looked upon as a stratified Monte Carlo sampling where the pairwise correlations can be minimized to a small value (which is essential for uncorrelated parameter estimates) or else set to a desired value . LHS is especially useful in exploring the interior of the parameter space, and for limiting the experiment to a fixed (user specified) number of runs. All designs are generated algorithmically, with the exception of Box-Behnken designs which use table lookups. Latin hypercube samples are generated via an interface to LHS software from Sandia National Laboratories .