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장봉수

Jang, Bongsoo
Computational Mathematical Science Lab.
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dc.citation.conferencePlace KO -
dc.citation.conferencePlace Online -
dc.citation.title International Conference & Symposium on AI Advancements in Hyperbolic and Parabolic PDEs -
dc.contributor.author Jang, Bongsoo -
dc.date.accessioned 2024-01-05T17:35:10Z -
dc.date.available 2024-01-05T17:35:10Z -
dc.date.created 2024-01-05 -
dc.date.issued 2023-10-20 -
dc.description.abstract The theory of derivatives of non-integer order goes back to the Leibniz’s note in his list to
L’Hospital, Sep 30, 1695, in which the meaning of the derivative of order one half is
discussed (Fractional-order). Fractional derivatives provide an excellent tool for the
description of memory and hereditary properties of various materials and processes. Due to
this reason, Fractional Mathematical Modeling(FMM) or Fractional-order (partial)
differential equations(FPDEs) have been successfully applied in physics, biology, applied
sciences, and engineering. In this talk, I discuss several difficulties in finding numerical
approximations for Frac- tional Mathematical Modeling, such as an expensive computational
cost. Also, I introduce recent research improvements to overcome these difficulties and new
engineering applica- tions in nanofluids. In addition, I introduce Fractional Physics-informed
neural networks (fPINNs), an extended variant of PINNs that utilize standard feedforward
neural networks (NN) while explicitly incorporating partial differential equations (PDEs)
into the neural network architecture via automatic differentiation.
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dc.identifier.bibliographicCitation International Conference & Symposium on AI Advancements in Hyperbolic and Parabolic PDEs -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/67747 -
dc.language 영어 -
dc.publisher University of Calicut and UNIST -
dc.title Fractional mathematical modeling and beyond -
dc.type Conference Paper -
dc.date.conferenceDate 2023-10-22 -

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