Advances in surrogate based modeling, feasibility analysis, and optimization: A review. (4th January 2018)

• Record Type: Journal Article
• Authors: Bhosekar, Atharv; Ierapetritou, Marianthi
• Is Part Of: Computers & chemical engineering. Volume 108(2018)
• Page Start: 250
• Page End: 267
• Publication Date: 2018-01-04
• Subjects: Surrogate models -- Derivative-free optimization -- Feasibility analysis -- Sampling -- Model selection; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.09.017
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: State of the art review of surrogate modeling techniques is provided. Best choice of the surrogate model depends on the type of problem at hand. Recent advances in sampling strategies for surrogate model building are discussed. Abstract: The idea of using a simpler surrogate to represent a complex phenomenon has gained increasing popularity over past three decades. Due to their ability to exploit the black-box nature of the problem and the attractive computational simplicity, surrogates have been studied by researchers in multiple scientific and engineering disciplines. Successful use of surrogates shall result in significant savings in terms of computational time and resources. However, with a wide variety of approaches available in the literature, the correct choice of surrogate is a difficult task. An important aspect of this choice is based on the type of problem at hand. This paper reviews recent advances in the area of surrogate models for problems in modeling, feasibility analysis, and optimization. Two of the frequently used surrogates, radial basis functions, and Kriging are tested on a variety of test problems. Finally, guidelines for the choice of appropriate surrogate model are discussed.