Global optimization of symbolic surrogate process models based on Bayesian learning

Tim Forster; Daniel Vázquez; Gonzalo Guillé-Gosálbez

doi:10.1016/b978-0-443-15274-0.50198-0

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Global optimization of symbolic surrogate process models based on Bayesian learning

Tim Forster Daniel Vázquez Gonzalo Guillé-Gosálbez

Year: 2023 Computer-aided chemical engineering/Computer aided chemical engineering Pages: 1241-1246 Publisher: Elsevier BV

DOI: 10.1016/b978-0-443-15274-0.50198-0

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Keywords:

Bayesian optimization Symbolic regression Computer science Global optimization Gaussian process Surrogate model Process (computing) Artificial intelligence Machine learning Artificial neural network Mathematical optimization Gaussian Algorithm Mathematics Genetic programming

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Advanced Multi-Objective Optimization Algorithms

Physical Sciences → Computer Science → Computational Theory and Mathematics

Advanced Control Systems Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Process Optimization and Integration

Physical Sciences → Engineering → Control and Systems Engineering

Global optimization of symbolic surrogate process models based on Bayesian learning

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