在自激活固体中通过局部规则学习刚性张量器
Yuxuan Tang1, Wenjing Ye1, Jingjing Jia2
1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 14, 2024
概括
这项研究引入了自我激活的固体,一种新型的活性超材料,可以从经历过的变形中学习所需的刚性特性. 这允许自主适应,以提高材料性能和功能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 机械超材料通常是为特定的功能而设计的.
- 现有的适应性材料在计算和协调方面面临挑战.
研究的目的:
- 开发活性格子超材料,称为自我激活固体,能够自主学习所需的刚度张量.
- 在适应性材料中克服离线计算和复杂元素协调的局限性.
主要方法:
- 介绍了一个简单的在线和本地学习策略,基于对比的Hebbian学习.
- 根据学习过程中仅基于局部应变的各种结合刚度.
- 利用数值测试来验证材料的学习能力.
主要成果:
- 自主激活的固体自主地实现了所需的散装,剪切和合模块.
- 证明了形成单模和双模极端材料的能力.
- 当暴露于不同的负载时,展示了时间变化的模块的实现.
结论:
- 拟议的设计适用于各种格子几何形状,并且坚固地抵抗损坏.
- 这种方法有利于自主材料,物理学习机器和自适应机器人的设计.
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