Extending a physics-based constitutive model using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7185
- @Article{KRONBERGER:2022:AES,
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author = "Gabriel Kronberger and Evgeniya Kabliman and
Johannes Kronsteiner and Michael Kommenda",
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title = "Extending a physics-based constitutive model using
genetic programming",
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journal = "Applications in Engineering Science",
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volume = "9",
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pages = "100080",
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year = "2022",
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ISSN = "2666-4968",
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DOI = "
doi:10.1016/j.apples.2021.100080",
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URL = "
https://www.sciencedirect.com/science/article/pii/S2666496821000431",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Material modelling, Flow stress",
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abstract = "In material science, models are derived to predict
emergent material properties (e.g. elasticity,
strength, conductivity) and their relations to
processing conditions. A major drawback is the
calibration of model parameters that depend on
processing conditions. Currently, these parameters must
be optimized to fit measured data since their relations
to processing conditions (e.g. deformation temperature,
strain rate) are not fully understood. We present a new
approach that identifies the functional dependency of
calibration parameters from processing conditions based
on genetic programming. We propose two (explicit and
implicit) methods to identify these dependencies and
generate short interpretable expressions. The approach
is used to extend a physics-based constitutive model
for deformation processes. This constitutive model
operates with internal material variables such as a
dislocation density and contains a number of
parameters, among them three calibration parameters.
The derived expressions extend the constitutive model
and replace the calibration parameters. Thus,
interpolation between various processing parameters is
enabled. Our results show that the implicit method is
computationally more expensive than the explicit
approach but also produces significantly better
results",
- }
Genetic Programming entries for
Gabriel Kronberger
Evgeniya Kabliman
Johannes Kronsteiner
Michael Kommenda
Citations