Deep Learning and physics 2019
Special introductory lecture

"Lectures on Machine Learning and String Theory"

1. Computational Complexity in Theoretical Physics
2. Introduction to Machine Learning
3. Machine Learning and the String Landscape

Lecturer : Prof. James Halverson (Northeastern University, USA)

13:30-17:00, October 30th (Wed), 2019 (The day before DLAP2019 conference)

Venue : H701 lecture room, department of physics, Osaka University (The room is located on the 7-th floor, Building H, School of Science, Toyonaka campus.) Campus map Access to Toyonaka campus

Theoretical physics is hard! Not just because of the advanced math that we have to learn, but also because doing computations can be tedious and theory space can be large. Computational complexity is a subject in computer science that quantifies the hardness of problems. I will introduce the subject and its appearance in theoretical physics, and will then introduce multiple different types of machine learning as tools for tackling hard problems, including supervised learning and reinforcement learning. I will then introduce one of the biggest datasets in theoretical physics, the landscape of string theory vacua, and explain how it is crucial for understanding the particle physics and cosmology implications of string theory. It will be argued that machine learning is essential for understanding the landscape, in part for reasons of computational complexity. Examples will be given.

Contact : Koji Hashimoto (Osaka U, DLAP2019 organizer)


Deep Learning and physics 2019 (DLAP2019)

High Energy Theory group, Osaka University

Quantum Information and Quantum Biology Division (QIQB), Osaka University