Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Sachin Jog; Daniel Vázquez; Lucas F. Santos; José A. Caballero; Gonzalo Guillén‐Gosálbez

doi:10.1016/j.compchemeng.2023.108563

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Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Sachin Jog Daniel Vázquez Lucas F. Santos José A. Caballero Gonzalo Guillén‐Gosálbez

Year: 2023 Journal: Computers & Chemical Engineering Vol: 182 Pages: 108563-108563 Publisher: Elsevier BV

DOI: 10.1016/j.compchemeng.2023.108563

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Keywords:

Surrogate model Bayesian probability Process (computing) Computer science Regression Bayesian linear regression Symbolic regression Bayesian optimization Machine learning Artificial intelligence Mathematical optimization Mathematics Bayesian inference Statistics Genetic programming

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Diverse Scientific and Engineering Research

Physical Sciences → Engineering → Computational Mechanics

Advanced Statistical Process Monitoring

Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty

Injection Molding Process and Properties

Physical Sciences → Engineering → Mechanical Engineering

Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

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