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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781665403207
DOIs
Publication statusPublished - Jun 8 2021
Event5th IEEE International Conference on Cybernetics, CYBCONF 2021 - Virtual, Sendai, Japan
Duration: Jun 8 2021Jun 10 2021

Publication series

Name2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021

Conference

Conference5th IEEE International Conference on Cybernetics, CYBCONF 2021
Country/TerritoryJapan
CityVirtual, Sendai
Period6/8/216/10/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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