Learning surrogate models for simulation‐based optimization

By Alison Cozad, Nikolaos V. Sahinidis, David C. Miller. 2014

444 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e44c9dcf90d5a9a7e74a1d74c9900ff69142c67f

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In Computational Theory and Mathematics

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In Management Science and Operations Research

Citations in the corpus

Design of computer experiments: A review (2017, methodology): “Recent works [24, 25, 64] have strived to use optimization strategies to achieve sample placement.”
Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “The approaches based on a spacefilling sequential design were also studied in Crombecq et al. (2011). Here, the authors developed a group of sequential sampling approaches which exhibit performance competitive with one-shot or stationary experimental designs.”
Rigorous design of distillation columns using surrogate models based on Kriging interpolation (2015, methodology): “Additionally, instead of considering all the simulation as a black box, some existing techniques disaggregate the simulation model into different blocks and model each block separately before optimization, ensuring that all relevant connectivity variables are modeled.(15) In this way, by disaggregat”
Expert Systems With Applications (unknown): “…analysis in the literature include linear regression ( Seber & Lee, 2012 ), automated learning of algebraic models for optimisation (ALAMO) ( Cozad, Sahinidis, & Miller, 2014; Zhang & Sahinidis, 2013 ), support vector regression (SVR) ( Smola & Schlkopf, 2004 ), multilayer perception (MLP)…”
Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, background): “Data-driven modeling approaches have become popular in recent years because of their wide applicability (Queipo et al., 2005; Wang & Shan, 2007; Cozad et al., 2014) and are used in one of the following three situations: (1) The existing model is too complex to be reduced to the desired degree by usi”