These results not only closely match the actual values but also demonstrate a substantial decrease in computational effort compared to the first 'blind' approach, making this approach particularly effective for quick initial evaluations. It is observed that with increasing the parameter search space dimension the number of iterations seem to accordingly increase in a nonlinear way, highlighting that the use of surrogate models for higher dimension inversion problems is necessary. For instance, 166 iterations were required for a 2D space compared to just 11 iterations for a 1D space.