Control of chaos in the Frenkel-Kontorova model using reinforcement learning

2020 ◽  
Author(s):  
You-Ming Lei ◽  
Yan-Yan Han
2021 ◽  
Author(s):  
Yanyan Han ◽  
Jianpeng Ding ◽  
Lin Du ◽  
Youming Lei

Abstract In this work, we propose a method of control and anti-control of chaos based on the moving largest Lyapunov exponent using reinforcement learning. In this method, we design a reward function for the reinforcement learning according to the moving largest Lyapunov exponent, which is similar to the moving average but computes the corresponding largest Lyapunov exponent using a recently updated time series with a fixed, short length. We adopt the density peaks-based clustering algorithm to determine a linear region of the average divergence index so that we can obtain the largest Lyapunov exponent of the small data set by fitting the slope of the linear region. We show that the proposed method is fast and easy to implement through controlling and anti-controlling typical systems such as the Henon map and Lorenz system.


Author(s):  
Z.M. Wang ◽  
J.P. Zhang

High resolution electron microscopy reveals that antiphase domain boundaries in β-Ni3Nb have a hexagonal unit cell with lattice parameters ah=aβ and ch=bβ, where aβ and bβ are of the orthogonal β matrix. (See Figure 1.) Some of these boundaries can creep “upstairs” leaving an incoherent area, as shown in region P. When the stepped boundaries meet each other, they do not lose their own character. Our consideration in this work is to estimate the influnce of the natural misfit δ{(ab-aβ)/aβ≠0}. Defining the displacement field at the boundary as a phase modulation Φ(x), following the Frenkel-Kontorova model [2], we consider the boundary area to be made up of a two unit chain, the upper portion of which can move and the lower portion of the β matrix type, assumed to be fixed. (See the schematic pattern in Figure 2(a)).


Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers

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