his bi-weekly seminar will focus on generative modeling while also exploring data-driven approaches for addressing key challenges in statistical physics, such as phase transitions, lattice structures, and Monte Carlo sampling techniques. Students will be assigned research papers that demonstrate the application of generative models to challenges in statistical physics, including but not limited to phase transitions, lattice structures, and Monte Carlo sampling techniques. With guidance from their academic supervisors, students will be expected to read, interpret, critique, and present key findings from selected papers. The focus will be on generative modeling methods such as variational autoencoders (VAEs), generative adversarial networks (GANs), Normalizing flows (NF) and diffusion model specifically in the context of statistical physics problems.
The seminar will be aimed at Master’s students, and a fundamental knowledge of both machine learning and statistical physics will be highly recommended.
- Trainer/in: Lorenz Linhardt
- Trainer/in: Ankur Singha