Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise

Feasibility analysis has been developed to evaluate and quantify the capability that a process can remain feasible under uncertainty of model inputs and parameters. It can be conducted during the design stage when the objective is to get a robust design which can tolerate a certain amount of variations in the process conditions. Also, it can be used after a design is fixed when the objective is to characterize its feasible region. In this work, we have extended the usage of feasibility analysis to the cases in which inherent stochasticity is existent in the model outputs. With a surrogate-based adaptive sampling framework, we have developed and compared three algorithms that are promising to make accurate predictions on the feasible regions with a limited sampling budget. Both the advantages and limitations are discussed based on the results from five benchmark problems. Finally, we apply such methods to a pharmaceutical manufacturing process and demonstrate its potential application in characterizing the design space of the process.