Zone-wise surrogate modelling (ZSM) of univariate systems. (June 2023)

• Record Type: Journal Article
• Authors: Srinivas, Srikar Venkataraman; Karimi, Iftekhar A
• Is Part Of: Computers & chemical engineering. Volume 174(2023)
• Publication Date: 2023-06
• Subjects: Zone-wise modelling -- Surrogate models -- Global optimization -- Machine learning -- Univariate systems; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2023.108249
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Zone-wise surrogate modelling (ZSM) algorithm for univariate systems. Automated zone identification based on a user-specified acceptable goodness of fit. Ability to select and switch best surrogates for each zone from a library. Facilitates effective global optimization of 1D multimodal functions. Abstract: Many complex systems display distinctly different behaviors across regions, zones, or sub-domains. A single surrogate may not suffice in modelling such systems. A better approach would be to identify the various zones and model them individually. In this work, we propose a zone-wise surrogate modelling (ZSM) algorithm to identify various zones in a system's input domain based on a user-specified acceptable goodness of fit and recommend the best surrogate for each identified zone from a library of potential surrogates. We have assessed ZSM on ten case studies involving complex 1-D functions and compared its modelling performance against some non-linear and piecewise models. We also show how ZSM can help in global optimization using five complex multimodal functions and found that a ZSM-based approach successfully identifies the true global optima of these functions. In future, we aim to extend ZSM for the modelling and optimization of complex multi-input single-output (MISO) systems.