Steel Phase Kinetics Modeling using Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Piringer:2022:SYNASC,

• author = "David Piringer and Bernhard Bloder and Gabriel Kronberger",
• booktitle = "2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
• title = "Steel Phase Kinetics Modeling using Symbolic Regression",
• year = "2022",
• pages = "327--330",
• abstract = "We describe an approach for empirical modelling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilatometer measurements and produces a system of differential equations that models the phase kinetics. Our initial results demonstrate that the proposed approach allows to identify compact differential equations that fit the data. The model predicts ferrite, pearlite and bainite formation for a single steel type. Martensite is not yet included in the model. Future work shall incorporate martensite and generalise to multiple steel types with different chemical compositions.",
• keywords = "genetic algorithms, genetic programming, Phase measurement, Scientific computing, Computational modelling, Differential equations, Predictive models, Prediction algorithms, Mathematical models, steel, phase kinetics, symbolic regression, dynamic models",
• DOI = "doi:10.1109/SYNASC57785.2022.00059",
• ISSN = "2470-881X",
• month = sep,
• notes = "Also known as \cite{10131020}",

}

Genetic Programming entries for David Piringer Bernhard Bloder Gabriel Kronberger

Citations