article · AIChE Journal · 2014
Learning surrogate models for simulation‐based optimization
Alison Cozad, Nikolaos V. Sahinidis, David C. Miller
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Abstract
A central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments is addressed. A methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible is proposed. The approach begins by building a low‐complexity surrogate model. The model is built using a best subset technique that leverages an integer programming formulation to allow for the efficient consideration of a large number of possible functional components in the model. The model is then improved systematically through the use of derivative‐free optimization solvers to adaptively sample new simulation or experimental points. Automated learning of algebraic models for optimization (ALAMO), the computational implementation of the proposed methodology, along with examples and extensive computational comparisons between ALAMO and a variety of machine learning techniques, including Latin hypercube sampling, simple least‐squares regression, and the lasso is described. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2211–2227, 2014
Cite this paper
Cozad, A., Sahinidis, N. V., & Miller, D. C. (2014). Learning surrogate models for simulation‐based optimization. AIChE Journal, 60(6), 2211–2227. https://doi.org/10.1002/aic.14418
Cozad, Alison, et al. “Learning surrogate models for simulation‐based optimization.” AIChE Journal, vol. 60, no. 6, 2014, pp. 2211–27, https://doi.org/10.1002/aic.14418.
Cozad, Alison, Nikolaos V. Sahinidis, and David C. Miller. 2014. “Learning surrogate models for simulation‐based optimization.” AIChE Journal 60 (6): 2211–27. https://doi.org/10.1002/aic.14418.
Cozad, A., Sahinidis, N.V. and Miller, D.C. (2014) ‘Learning surrogate models for simulation‐based optimization’, AIChE Journal, 60(6), pp. 2211–2227. Available at: https://doi.org/10.1002/aic.14418.
@article{cozad2014learning,
title = {{Learning surrogate models for simulation‐based optimization}},
author = {Cozad, Alison and Sahinidis, Nikolaos V. and Miller, David C.},
journal = {AIChE Journal},
volume = {60},
number = {6},
pages = {2211--2227},
year = {2014},
doi = {10.1002/aic.14418}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.