Conventional sampling methods can lead to under/over-sampling issues. Our hypothesis is to develop a workflow to reduce the time spent on data generation by reducing the total number of the required data points, and improving the data effectiveness (or reducing the average time spent on generating an individual data point). To successfully set up such a methodology, it is beneficial to review prior work on surrogate modelling and smart sampling, which will be elaborated in the remainder of this section.