基于物理学的神经网络用于可编程的原木元材料,可控制部署.
Sukheon Kang1, Youngkwon Kim2, Jinkyu Yang2
1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. ryush@kaist.ac.kr.
Materials horizons
|November 19, 2025
概括
本研究介绍了一个无数据的物理信息神经网络 (PINN),用于设计原木结构. 该框架允许精确控制可部署的元材料中的机械能景观.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 灵感来自于Origami的结构提供了轻量级,可部署的系统,具有可编程的机械性能.
- 由于非线性力学,多稳定性和精确的部署力控制要求,设计这些结构是复杂的.
研究的目的:
- 开发一个基于物理学的神经网络 (PINN) 框架,用于对形克雷斯林原形 (CKO) 的前预测和反向设计.
- 为了实现复杂的机械能量景观的无数据设计,在原木灵感的元材料中.
主要方法:
- 开发了一个PINN框架,将机械平衡方程直接嵌入到学习过程中.
- 该模型对能源景观进行前预测,并对目标稳定状态高度和能源障碍进行反向设计.
- 这种方法被扩展到等级CKO组件,用于连续的层次部署.
主要成果:
- 该PINN框架准确地预测完整的能源景观,最小的非物理文物.
- 反向设计程序允许自由编程能量曲线,包括稳定状态和能量障碍.
- 通过有限元模拟和物理原型的验证证实了设计的部署序列和屏障比率.
结论:
- 这项工作介绍了一种多功能,无数据的方法,用于编程机械能景观,在原木灵感的元材料中.
- 该方法促进了可部署的航空航天系统,变形结构和软机器人执行器的设计.
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