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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.
