Fractional-Order Modeling of Heat and Moisture Transfer in Anisotropic Materials Using a Physics-Informed Neural Network

被引:1
|
作者
Sokolovskyy, Yaroslav [1 ]
Drozd, Kazimierz [2 ]
Samotii, Tetiana [3 ]
Boretska, Iryna [4 ]
机构
[1] Lviv Polytech Natl Univ, Dept Comp Aided Design, 12 S Bandery St, UA-79013 Lvov, Ukraine
[2] Lublin Univ Technol, Fac Mech Engn, Dept Mat Engn, 36 Nadbystrzycka St, PL-20618 Lublin, Poland
[3] Ukrainian Natl Forestry Univ, Dept Software Engn, 103 Gen Chuprynky St, UA-79057 Lvov, Ukraine
[4] Ukrainian Natl Forestry Univ, Dept Comp Sci, 103 Gen Chuprynky St, UA-79057 Lvov, Ukraine
关键词
fractal structure; self-organization; heat-and-mass exchange; fractional-differential apparatus; step-by-step learning; fractal neural method;
D O I
10.3390/ma17194753
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Mathematical models of heat and moisture transfer for anisotropic materials, based on the use of the fractional calculus of integro-differentiation, are considered because such two-factor fractal models have not been proposed in the literature so far. The numerical implementation of mathematical models for determining changes in heat exchange and moisture exchange is based on the adaptation of the fractal neural network method, grounded in the physics of processes. A fractal physics-informed neural network architecture with a decoupled structure is proposed, based on loss functions informed by the physical process under study. Fractional differential formulas are applied to the expressions of non-integer operators, and finite difference schemes are developed for all components of the loss functions. A step-by-step method for network training is proposed. An algorithm for the implementation of the fractal physics-informed neural network is developed. The efficiency of the new method is substantiated by comparing the obtained numerical results with numerical approximation by finite differences and experimental data for particular cases.
引用
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页数:23
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