Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods
Created by W.Langdon from
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- @Article{angelis:2023:Micromachines,
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author = "Dimitrios Angelis and Filippos Sofos and
Konstantinos Papastamatiou and Theodoros E. Karakasidis",
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title = "Fluid Properties Extraction in Confined Nanochannels
with Molecular Dynamics and Symbolic Regression
Methods",
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journal = "Micromachines",
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year = "2023",
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volume = "14",
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number = "7",
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pages = "Article No. 1446",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2072-666X",
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URL = "https://www.mdpi.com/2072-666X/14/7/1446",
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DOI = "doi:10.3390/mi14071446",
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abstract = "In this paper, we propose an alternative road to
calculate the transport coefficients of fluids and the
slip length inside nano-conduits in a Poiseuille-like
geometry. These are all computationally demanding
properties that depend on dynamic, thermal, and
geometrical characteristics of the implied fluid and
the wall material. By introducing the genetic
programming-based method of symbolic regression, we are
able to derive interpretable data-based mathematical
expressions based on previous molecular dynamics
simulation data. Emphasis is placed on the physical
interpretability of the symbolic expressions. The
outcome is a set of mathematical equations, with
reduced complexity and increased accuracy, that adhere
to existing domain knowledge and can be exploited in
fluid property interpolation and extrapolation,
bypassing timely simulations when possible.",
-
notes = "also known as \cite{mi14071446}",
- }
Genetic Programming entries for
Dimitrios Angelis
Filippos Sofos
Konstantinos Papastamatiou
Theodoros E Karakasidis
Citations