PRESTO: Predictive REcommendation of Surrogate models To approximate and Optimize

Abstract

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate. Many techniques exist for surrogate modeling; however, selecting suitable techniques for a given application remains an open challenge. This work describes PRESTO, a Random Forest classifier-based tool, to recommend appropriate surrogate modeling techniques for a given dataset for surface approximation and surrogate-based optimization, using attributes calculated only using the input and output data. The tool identifies the techniques for surface approximation with an accuracy of 91% and a precision of 90% and for surrogate-based optimization with an accuracy of 98% and a precision of 99%. PRESTO was tested on data generated from a high fidelity process model of the cumene production process. Its performance on this case study was comparable to the training data. PRESTO enables computational time savings for selecting surrogate model forms by avoiding expensive trial-and-error methods.