YoVDO

Dynamical Mean-Field Theory in Non-Equilibrium Many-Body Statistical Physics - Giulio Biroli

Offered By: Kavli Institute for Theoretical Physics via YouTube

Tags

Entanglement Dynamics Courses Aging Courses Dynamical Mean Field Theory Courses Short-Time Universality Courses

Course Description

Overview

Explore the cutting-edge applications of Dynamical Mean-Field Theory in non-equilibrium many-body statistical physics through this 31-minute conference talk delivered by Giulio Biroli at the Kavli Institute for Theoretical Physics. Delve into advanced topics such as aging, glassy dynamics, and high-dimensional chaos as part of the 2021 Non-Equilibrium Universality in Many-Body Physics KITP Conference. Gain insights into the emerging field of non-equilibrium many-body physics, sparked by the advent of quantum simulators, and discover how it bridges diverse scientific disciplines including statistical physics, AMO, condensed matter, and high-energy physics. Examine novel phases of matter far from equilibrium and their associated universality classes, exploring concepts like short-time universality, entanglement dynamics, and mappings between classical and quantum non-equilibrium systems. Understand the potential for cross-pollination between high-energy physics and non-equilibrium condensed and AMO systems, with a focus on experimental realizations that can enhance our understanding of far-from-equilibrium universality.

Syllabus

Dynamical Mean-Field Theory in Non-Equilibrium Many-Body Statistical Physics... ▸ Giulio Biroli


Taught by

Kavli Institute for Theoretical Physics

Related Courses

Drugs and the Brain
California Institute of Technology via Coursera
Understanding Dementia
University of Tasmania via Independent
Your Body in the World: Adapting to Your Next Big Adventure
Stanford University via Stanford OpenEdx
Science at the Polls: Biology for Voters, Part 2
University of California, Berkeley via edX
The Musculoskeletal System: The Science of Staying Active into Old Age
Newcastle University via FutureLearn