TRANSFORMING PHYSICS EDUCATION WITH NEURAL NETWORKS: MODERN APPROACHES AND TOOLS

Abstract

This article explores the current trend of using neural networks in teaching physics at the university level. The topic's relevance stems from the need to transform traditional teaching methods to meet the expectations of a new generation of students accustomed to interactive formats and digital technologies. The study aims to analyze modern neural network technologies employed in teaching various sections of physics, evaluate their effectiveness, and outline prospects for further development in this area. The research is based on a review of scientific publications on the subject and practical experiences of implementing neural network technologies in leading universities worldwide. The methodology involves systematic analysis, comparison, and generalization of existing neural network solutions. A detailed analysis of specific neural network technologies applied to different branches of physics is presented: long short-term memory (LSTM) and convolutional neural networks (CNN) for mechanics; generative adversarial network (GAN) and graph neural networks (GNN) for electromagnetism; deep reinforcement learning network (DRL) for thermodynamics; variational autoencoders network (VAE) and residual network (ResNet) for quantum physics; and deep convolutional networks and transformers for astrophysics.

The results demonstrate that implementing neural network technologies significantly enhances learning efficiency, facilitates the visualization of complex physical processes, automates computations, and enables personalized learning. It has been established that the application of various neural network architectures in the educational process fosters the development of critical thinking, a deeper understanding of physical concepts, and practical data-handling skills among students. Promising directions for further development include the creation of multimodal systems, the development of adaptive learning platforms, and the integration of virtual reality with neural network models.