DOI
10.34229/KCA2522-9664.26.3.16
UDC 004.852; 519.676
I.V. Boyko
Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine,
boyko.i.v.theory@gmail.com
O.A. Pastukh
Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine,
ol_pas@tntu.edu.ua
NUMERICAL METHODS FOR PROBABILISTIC IDENTIFICATION OF SPATIAL
CHARACTERISTICS OF LOW-DIMENSIONAL SYSTEMS USING MACHINE LEARNING
Abstract. A general methodology for identifying the spatial characteristics of low-dimensional systems with arbitrary geometric configurations has been developed by applying machine learning methods to datasets obtained from experimental measurements. A numerical approach has been developed that implements mathematical models for spectral problems, enabling a unified representation of input data from training datasets as normalized tabular dependencies. A neural network was used on 560 datasets to identify precision parameters of nanosystems and confinement. The results obtained, compared to those obtained using direct numerical methods, are characterized by 30–40 times shorter computational time and consistently high accuracy. The developed approach has potential for application as a tool for automating the processing of large volumes of experimental and calculated data in nanotechnology and electronics.
Keywords: machine learning, neural network, low-dimensional systems, nanostructures.
full text
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