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细胞自动机启发了机械学习的多稳定原始形态元材料

Zuolin Liu^1,2, Hongbin Fang¹, Jian Xu¹

¹Institute of AI and Robotics, State Key Laboratory of Medical Neurobiology, MOE Engineering Research Center of AI & Robotics, Fudan University, Shanghai, 200433, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|October 23, 2023

概括

智能材料现在可以使用多稳定的原始元材料和储计算来执行复杂的计算. 这种新的框架消除了对传统逻辑门的需求,为手写识别等任务提供了先进的基于材料的计算.

科学领域:

材料科学材料科学材料科学
计算科学计算科学
机械工程机械工程

背景情况:

多稳定的元材料显示结构变化和布尔逻辑运算之间的联系.
智能材料的当前计算框架需要复杂的逻辑门网络,这给制造和信号传播带来了挑战.
细胞自动机的原理为材料的新型计算方法提供了灵感.

研究的目的:

提出一个新的计算框架,用于使用储库计算的多稳定原始元材料.
在基于材料的计算中克服传统逻辑门网络的局限性.
为了证明使用单个执行器系统的高水平计算的可行性.

主要方法:

开发了一个计算框架,将储水库计算与多稳定的原始金属材料集成在一起.
利用一个多稳定的堆叠的Miura-origami超材料作为实验验证的平台.
实现了数字识别,手写识别和5位内存任务.

主要成果:

在不需要逻辑门网络的情况下成功演示了高级计算.
用一个单一的执行器实现了数字识别,该执行器位于原木元材料上.
复杂任务的可行性,如手写识别和记忆功能,经过实验验证.

结论:

拟议的框架使智能材料具有先进的计算能力.
这种方法显著减少了与大规模材料网络相关的制造复杂性和信号传播问题.
代表着对材料机械智能和计算中的变革应用的重大进步.

关键词:

图像识别功能图像识别功能机械智能是一种机械智能.机械记忆机械记忆是一种机械记忆.机械超材料机械超材料物理计算物理计算

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