Evrişimli sinir ağı ile 2-boyutlu ısing model konfigürasyonlarının sıcaklık temelli analizi

This study investigates the application of Convolutional Neural Networks (CNNs) to classify data associated with complex physical systems, specifically focusing on the Two-Dimensional Ising Model and its temperature-dependent ferromagnetic be- haviour. The motivation behind this work is to leverage CNNs for distinguishing between phases of the system under different temperature regions: below critical, at critical, and above critical temperatures. Three distinct neural network architectures were implemented, and their performance was evaluated using a dataset of simulated configurations. The results demonstrate that the models achieved high accuracy in predicting the temperature-related phases, indicating the effectiveness of CNNs in capturing physical patterns. These findings contribute to the understanding of CNNs in physical sciences and suggest that machine learning techniques could potentially be applied to other complex systems. Future work might focus on refining the model architectures and extending the analysis to other types of lattice structures.