Memory Effect, Rejuvenation and Chaos Effect in the Multi-layer Random Energy Model
Munetaka Sasaki, Koji Nemoto
Abstract
We introduce magnetization to the Multi-layer Random Energy Model which has a hierarchical structure, and perform Monte Carlo simulation to observe the behavior of ac-susceptibility. We find that this model is able to reproduce three prominent features of spin glasses, i.e., memory effect, rejuvenation and chaos effect, which were found recently by various experiments on aging phenomena with temperature variations.
Create a lesson
Related papers
On physical implementability of reinforcement-learning-driven reasoning
Eiji Konishi
The Stone-Wales trefoil: a new topological defect in graphene
M. V. Kondrin, Y. B. Lebed, V. V. Brazhkin
Short-Range Vector Spin Glasses: Parisi Hierarchies and Real-Space Ultrametricity
Akash Vijay, Jaewon Kim, Jong Yeon Lee
Lyapunov spectrum of random neural networks
David G. Clark
Revealing more on complex energy landscapes by passing less local information: Cavity approach with trust region in non-convex optimization problems
Zhiqi Xu, Chi Ho Yeung
Predicting activation-barrier and plasticity-onset statistics in a model of glasses
Makoto Suda, Edan Lerner, Eran Bouchbinder