Feasibility of Machine Learning on PhaseTransition of Ising Model
- Type
- Thesis
- Degree
- Master
- Department
- 대학원 물리·광과학과
- Advisor
- Hwang, Chi-Ok
- Abstract
- Ising models are simple computational models that can describe magnetism of materials. It is well known that there is a phase transition in two-dimensional square lattice Ising model, and the analytic solution is also known. To find the Curie temperature of the two-dimensional square lattice Ising model, we can use machine learning classifying the configuration data. In addition, we will perform basic researches that can utilize machine learning for obtaining the Curie temperatures for complex Ising models by using the trained model through the two-dimensional square lattice Ising model.
- Alternative Author(s)
- 이정우
- Appears in Collections:
- Department of Physics and Photon Science > 3. Theses(Master)
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