Parameter Optimization via CMA-ES for Implementation in the Active Control of Magnetic Pillar Arrays

Suparat Gaysornkaew, Danilo Vasconcellos Vargas, F. Tsumori
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Abstract

Pillared surfaces are the products of a surface modification technique that allow the implementation of active control methods by an outer source such as magnetic fields. Pillar arrays with magnetic tips exhibit different characteristics depending on the initial positional arrangement of the pillars and/or the environmental magnetic field conditions. This study develops methods for simulation and parameter optimization by machine learning to aid the investigation of pillar behaviors in various combinations of initial positions and magnetic fields. Optimization is performed using the co-variance adaptation evolution strategy (CMA-ES). The algorithm is tested to obtain preliminary results: (1) the maximum size of the pillar pitch at a given magnetic field; (2) the initial pillar arrangement of a 3-pillar unit cell and three settings of applied magnetic field–each corresponds to a predefined contact state of a three-stage paring pattern.
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基于CMA-ES的磁柱阵主动控制参数优化实现
柱状表面是表面改性技术的产物,该技术允许通过外部源(如磁场)实现主动控制方法。具有磁尖的柱阵列根据柱的初始位置排列和/或环境磁场条件表现出不同的特性。本研究开发了通过机器学习进行模拟和参数优化的方法,以帮助研究柱在不同初始位置和磁场组合下的行为。采用协方差适应进化策略(CMA-ES)进行优化。对该算法进行了测试,得到了初步结果:(1)给定磁场下的最大柱距尺寸;(2)三柱单元电池的初始柱状排列和三种外加磁场设置,每种设置对应于三级配对模式的预定义接触状态。
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