fractional derivatives

ADAPTIVE FRACTIONAL NEURAL ALGORITHM FOR MODELING HEAT-AND-MASS TRANSFER

A fractional neural network with an adaptive learning rate has been proposed for modeling the dynamics of non-isothermal heat and mass transfer in capillary-porous materials, taking into account the memory effect and spatial nonlocality. The proposed approach employs a decoupled neural network architecture based on loss functions that reflect the physical characteristics of the investigated process. A stepwise training method is utilized to reduce sensitivity to errors and disruptions.

Microscopic theory of the influence of dipole superparamagnetics (type <beta-CD<FeSO_4>>) on current flow in semiconductor layered structures (type GaSe, InSe)

A statistical approach to description of the charge carrier transfer processes in hybrid nanostructures taking into account electromagnetic fields is proposed using the method of the nonequilibrium statistical operator Zubarev. Generalized transfer equations are obtained, which describe non-Markov processes of charge transfer in the system taking into account magnetic and polarization processes under the influence of external and induced internal electromagnetic fields.