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学习复杂性引导光诱导的自我组织的纳米模式.

Eduardo Brandao1, Anthony Nakhoul1, Stefan Duffner2

  • 1Université Jean Monnet Saint-Etienne, CNRS, IOGS, Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France.

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超快激光照射通过自我组织创建纳米尺寸的表面图案. 一个新的深度学习模型预测了这些模式,使得激光制造中的受控材料操纵成为可能.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 激光物理 激光物理
  • 计算物理 计算物理

背景情况:

  • 超快激光照射诱导自发的表面自我组织成纳米级散射结构.
  • 这些模式源于类似于雷利-贝纳德不稳定的对称性破坏动态.

研究的目的:

  • 在2D中数值地解开不同表面图案对称性的共存和竞争.
  • 开发激光诱导表面自我组织的预测模型.

主要方法:

  • 使用随机通用Swift-Hohenberg模型进行数值模拟.
  • 提出了一种用于模式识别的新型深卷积神经网络 (CNN).
  • 采用物理引导的机器学习策略,用显微镜数据对模型进行校准.

主要成果:

  • 成功识别并学习稳定特定表面图案的主导模式.
  • 证明了尺度不变性并与实验显微镜数据对CNN进行了验证.
  • 该方法准确地从稀疏的非时间序列数据中预测模式形成.

结论:

  • 开发的深度学习方法可以预测所需的自我组织模式.
  • 该方法允许识别用于受控材料结构的实验条件.
  • 在激光制造中使用受控光学场为监督局部物质操纵铺平了道路.