In the input space of a kinetic model, the points resulting in high quality outcomes (i.e., high quality points) may not be concentrated. One naive way to find all the high quality points is complete profiling, i.e., to map all the grid points in the input space to the outcome space through a kinetic model (Figure ). By applying cluster analysis to these points, diverse strategies to realize a high-quality chemical process can be discovered. An obvious drawback of complete profiling is, however, that its computational cost is exponential to the number of input variables. Using a black-box optimization method, 4 some high quality points can be discovered quickly, but the whole picture cannot be obtained. Kusumo et al. proposed a method to identify a level set of the design space (i.e. the set of high quality points) using nested sampling, which is more efficient than complete profiling but would not capture the entirety of inter-variable relations.