Multi-Objective Discovery of PDE Systems Using Evolutionary Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InProceedings{Maslyaev:2021:CEC,
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author = "Mikhail Maslyaev and Alexander Hvatov",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Multi-Objective Discovery of PDE Systems Using
Evolutionary Approach",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "596--603",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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keywords = "genetic algorithms, genetic programming, equation
discovery, system discovery, partial differential
equation, PDE, data-driven methods, multi-objective
optimization, MOEA/DD",
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isbn13 = "978-1-7281-8393-0",
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URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt",
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URL = "https://www.human-competitive.org/sites/default/files/multi-objective_discovery_of_pde_systems_using_evolutionary_approach.pdf",
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DOI = "doi:10.1109/CEC45853.2021.9504712",
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size = "8 pages",
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abstract = "Usually, the data-driven methods of the systems of
partial differential equations (PDEs) discovery are
limited to the scenarios, when the result can be
manifested as the single vector equation form. However,
this approach restricts the application to the real
cases, where, for example, the form of the external
forcing is of interest for the researcher and can not
be described by the component of the vector equation.
In the paper, a multi-objective co-evolution algorithm
is proposed. The single equations within the system and
the system itself are evolved simultaneously to obtain
the system. This approach allows discovering the
systems with the form-independent equations. In
contrast to the single vector equation, a
component-wise system is more suitable for expert
interpretation and, therefore, for applications. The
example of the two-dimensional Navier-Stokes equation
is considered.",
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notes = "Also known as \cite{9504712} Entered 2022 HUMIES",
- }
Genetic Programming entries for
Mikhail A Maslyaev
Alexander A Hvatov
Citations