Learning implicit yield surface models with uncertainty quantification for noisy datasets
Created by W.Langdon from
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- @Article{Birky:2025:cma,
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author = "Donovan Birky and John Emery and Craig Hamel and
Jacob Hochhalter",
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title = "Learning implicit yield surface models with
uncertainty quantification for noisy datasets",
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journal = "Computer Methods in Applied Mechanics and
Engineering",
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year = "2025",
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volume = "436",
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pages = "117738",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Machine learning, Uncertainty
quantification",
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ISSN = "0045-7825",
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URL = "
https://www.sciencedirect.com/science/article/pii/S0045782525000106",
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DOI = "
doi:10.1016/j.cma.2025.117738",
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abstract = "Materials often exhibit stochastic mechanical
behaviours due to their inherent intrinsic variability.
Data acquisition also introduces extrinsic noise into
data. To learn yield surface models under uncertainty,
we present a method that uses genetic programming based
symbolic regression (GPSR) and a multi-objective
fitness function (MOSR). Previous works have
demonstrated using an implicit fitness metric in GPSR
that compares the partial derivatives of proposed
models with those of the data, allowing the generation
of mechanics-guided, implicit yield surface models.
MOSR adds to that a Bayesian fitness metric to
simultaneously quantify parameter uncertainty. We test
this method on benchmark implicit and physical test
problems to demonstrate MOSR's efficacy in finding
implicit model forms on noisy data compared to the
conventional implicit fitness metric. The results show
that the MOSR algorithm prevents overfitting to noisy
data, improves parameter estimates on data even with no
noise present, and reduces model complexity, improving
overall model interpretability. The MOSR method affords
the ability to learn new and improved yield surface
models while simultaneously quantifying the uncertainty
in model parameters, leading to enhanced model
interpretability",
- }
Genetic Programming entries for
Donovan Keith Birky
John M Emery
Craig Hamel
Jacob Dean Hochhalter
Citations