Unsupervised learning of phase transition in quantum spin model
This report investigates the application of unsupervised learning techniques to detect phase transitions in quantum spin systems, focusing on the 2D and 3D Ising models. Using Monte Carlo simulations, we generate spin configurations and apply Principal Component Analysis (PCA), autoencoders, and...
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2025
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/184488 |
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