When applying BO in an experimental context, the target function can be any measurable output from the experiment. We use the BO process to determine how the target function depends on the design variables, such as adjustable experimental parameters, while performing as few experiments as possible. During a BO iteration, new experiments are performed using the values for the design variables suggested by the acquisition function. Experiments are subsequently performed to determine the experimental target values, which are used together with the processing conditions to update the surrogate model Fig. . The next iteration then proceeds using the updated surrogate model and this cycle is repeated until convergence is observed. Next, we consider the practical details of implementing this workflow.