强大的物理信息神经网络方法,用于从噪音移位数据中估计异质弹性特性
Tatthapong Srikitrungruang1, Sina Aghaee Dabaghan Fard1, Matthew Lemon1
1Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 25, 2025
概括
这项研究引入了一种新的反弹性物理信息神经网络 (IE-PINN),用于从噪声数据中准确地估计材料属性. IE-PINN克服了现有方法的局限性,使弹性参数的可靠重建成为可能.
科学领域:
- 计算力学 计算力学 计算力学
- 应用物理 应用物理
- 机器学习 机器学习
背景情况:
- 从噪音位移数据中估计异质弹性参数 (模量,波松比) 在反弹性问题中具有挑战性.
- 目前的方法存在不稳定性,噪声敏感性和难以确定模量的绝对尺度.
研究的目的:
- 开发一种可靠的方法,从噪音位移测量中重建空间异质弹性分布.
- 克服现有的反向估计技术的局限性,特别是在处理噪声和确定绝对材料特性方面.
主要方法:
- 开发了一种新的反弹性物理信息神经网络 (IE-PINN),集成了三个神经网络,用于位移,应变和弹性场.
- 采用了两阶段的估计策略:相对分布恢复,然后使用边界条件进行绝对尺度校准.
- 方法上的创新包括位置编码,正弦激活函数和顺序预训练,以提高性能和强度.
主要成果:
- IE-PINN证明了异质弹性分布的稳健重建,即使有显著的测量噪声.
- 两阶段的策略成功地恢复了相对空间分布和扬模块的绝对尺度.
- 拟议的方法在噪音条件下在准确性和稳定性方面明显优于现有技术.
结论:
- 该IE-PINN提供了一个准确而稳定的解决方案,用于从噪音位移数据中估计绝对尺度弹性参数.
- 这一进步对临床成像诊断和噪声普遍存在的机械表征等应用具有重大意义.
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