The training process for SVM is similar to the steps for surrogate training. Two differences are specified here. Firstly, only the dataset in the initial several iterations is used to train the classifier. This is because the classifier in this work is expected to give a rough classification between infeasible and feasible design spaces, so the iterative refinement for the classifier is not necessary. Secondly, the output for the classifier is binary, 0 and 1: set 0 if the simulation outputs fall on the infeasible space, while setting 1 if the simulation outputs fall on the feasible area. Following this, the data inputs together with the classifier outputs are used to train the SVM.