Learning Physics with Machine learning

Seyed Alireza Seif Tabrizi

University of Chicago

Wed, February 23, 2022 - 4:00 PM
Hannan 108


Machine learning methods have emerged as exciting tools to study problems in statistical and condensed matter physics, such as classifying phases of matter, detecting order parameters and generating configurations of a system from observed data. The recent success of applications of machine learning and artificial intelligence in physics begs the question of whether these techniques can speed up scientific discovery. In this talk I will discuss applications of machine learning in the study of thermodynamics, many-body quantum physics, and quantum simulations. Our results provide a new interpretable approach that can accelerate the study of physics in these systems.

This talk is based on:
Nat. Phys. 17, 105 (2021)
arXiv:2104.04453
arXiv:2106.13485
arXiv:2111.02385

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