Generative Machine Learning for Particle and Statistical Physics

By Vipul Arora and Ankur Singha

Presentation:

• presentation.pdf contains the tutorial slides.
• Contents:
  • Probability density functions and sampling
  • Transformation of random variables
  • Generative Adversarial Networks (GANs) for sampling
  • Rejection Sampling
  • Iterative Sampling Methods: Markov chain Monte Carlo (MCMC) sampling
  • Metropolis Hastings (MH) algorithm
  • Normalizing Flow (NF)

Codes:

• Codes are available here.
• These implement MCMC (Hamiltonian Monte Carlo) method and Normalizing Flow method for scalar field theory or $\phi^4$ theory.