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Computational Physics B

Course materials for Computational Physics B at University of Science and Technology of China (USTC), 2024 Fall.

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Course Content

This repository contains TA session materials covering the following topics:

TA Session 1: Monte Carlo Methods

  • Programming Basics: C, Python, MATLAB, Julia, R
  • Tools: Jupyter Notebook, Anaconda, VS Code/Cursor
  • Stochastic Processes: Random processes, Markov chains, Poisson processes
  • MCMC: Metropolis-Hastings algorithm, Gibbs sampling
  • Notebooks:
    • Why_Metropolis.ipynb - Understanding the Metropolis algorithm
    • Why_Variance_of_Monte_Carlo_Is_Important.ipynb - Variance reduction techniques
    • Weird_Property_of_High_Dim.ipynb - High-dimensional sampling properties
    • Why_Monte_Carlo_in_High_Dim.ipynb - MC in high dimensions
    • Metropolis_in_High_Dim.ipynb - High-dimensional Metropolis
    • Why_Class.ipynb - Object-oriented programming for physics simulations

TA Session 2: (Content TBD)

TA Session 3: Machine Learning Basics

  • Classification Problems:
    • Iris.ipynb - Iris dataset classification
    • Titanic.ipynb - Titanic survival prediction
  • Notes:
    • ML_Note_Yixuan.md - Machine learning notes

Repository Structure

.
├── TA_Session_1/          # Monte Carlo methods and stochastic processes
│   ├── *.ipynb            # Jupyter notebooks
│   ├── *.py               # Python scripts
│   └── TA_Session_1.md    # Session notes
├── TA_Session_2/          # (Upcoming content)
├── TA_Session_3/          # Machine learning basics
│   ├── Iris.ipynb
│   ├── ML_Note_Yixuan.md
│   └── Titanic/
└── Readme.md              # This file

Getting Started

Prerequisites

  • Python 3.x
  • Jupyter Notebook
  • NumPy, Matplotlib, SciPy

Installation

# Clone the repository
git clone <repository-url>

# Install dependencies
pip install numpy matplotlib scipy jupyter

# Start Jupyter
jupyter notebook

Homework Reference

  • HW1: Vectorization
  • HW2: Inverse transform sampling
  • HW3: Metropolis algorithm
  • HW4: High-dimensional sampling

Related Projects

License

This repository is for educational purposes only.