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  3. Volume 17, 2025
  4. Recognition of Inclusion Characteristics Using Neural Network Methods in Stationary Process Modeling

Recognition of Inclusion Characteristics Using Neural Network Methods in Stationary Process Modeling

SISN.
2025;
Volume 17
: pp. 75 - 92
https://doi.org/10.23939/sisn2025.17.075
Authors:
  1. Roman Havdulskyi
  2. Liubov Zhuravchak
1
Lviv Polytechnic National University, Software Development Department, Lviv, Ukraine
2
Lviv Polytechnic National University, Software Engineering Department

Detection and identification of inclusions in the modeling of stationary processes is a crucial task in many technical fields, including materials science, electronics, and non-destructive testing. The presence of inclusions can affect the mechanical, thermal, and electrical properties of a material, making the accurate determination of their geometric and physical characteristics essential. The use of modern numerical methods and deep learning techniques opens new opportunities for improving the efficiency and accuracy of prediction results.
This paper explores the application of the indirect near-boundary element method for solving direct problems in potential theory and various types of neural networks for solving inverse problems, i.e., recognizing the specified characteristics of inclusions (their location, size, and conductivity coefficient). A series of computational experiments were conducted to solve the direct problem, allowing the generation of datasets for training and testing models utilizing convolutional, recurrent, and deep neural networks.
To enhance data analysis efficiency, the application of data normalization techniques was tested. A novel approach to feature extraction for predicting the physical characteristic of inclusions (conductivity coefficient) was proposed, significantly improving the accuracy of its computation. After extensive testing, several neural network architectures were selected for determining the geometric and physical characteristics, and neural network ensembles were constructed to integrate the best aspects of each model. The results of ensemble model testing demonstrated higher accuracy in determining the conductivity coefficient and geometric characteristics of inclusions compared to individual models.
The obtained results may contribute to the advancement of non-destructive testing methods and the detection of defects or inclusions. Future research will focus on developing an approach for identifying the characteristics of multiple inclusions and creating more diverse datasets.

neural networks
inclusion recognition
stationary processes
convolutional neural networks
machine learning
non-destructive testing
potential theory
computational experiment
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