The bounds of the global optimization over the surrogate model are determined by the parameter sets used to build the surrogate. For each parameter 𝜃 𝑖 in the parameter vector 𝜃, the bounds for 𝜃 𝑖 are determined by the minimum (min(𝜃 𝑖 )) and maximum value (max(𝜃 𝑖 )) of 𝜃 𝑖 in the set of parameter vectors Θ = [𝜃 1 , 𝜃 2 , … , 𝜃 𝑁 ] used to build the surrogate. We then apply a small expansion factor 𝜂 to the parameter range, so that the optimization algorithm can search outside of the initial simulation box (described in Section 2.4.2). To expand the box, we multiply max(𝜃 𝑖 ) by 𝜂, and divide min(𝜃 𝑖 ) by 𝜂 to form the bounds box [LB(𝜃 𝑖 ), UB(𝜃 𝑖 )]. We chose the value of 𝜂 to be 1.1, expanding the box 10% in each direction, to allow the optimizer to search aggressively. This process is repeated for each parameter 𝜃 𝑖 ∈ 𝜃 to form an N-dimensional box, and is described in Equation .