基于多层次物理的深度学习,用于解决计算结构力学中的部分微分方程
Weiwei He1, Jinzhao Li1, Xuan Kong2
1College of Civil Engineering, Hunan University, Changsha, 410082, China.
Communications engineering
|November 2, 2024
概括
一个新的多层次物理信息神经网络 (PINN) 框架解决了结构力学方面的挑战. 这种方法提高了解决工程中复杂,高阶部分微分方程的精度和计算时间.
科学领域:
- 计算力学 计算力学 计算力学
- 在工程领域的人工智能.
- 应用数学 应用数学 应用数学
背景情况:
- 基于物理学的神经网络 (PINNs) 显示出解决部分微分方程的前景.
- 在结构力学中解决高阶非线性方程仍然是传统PINNs的计算挑战.
研究的目的:
- 为结构力学问题开发一个先进的多层次PINN框架.
- 克服经典神经网络在处理复杂,第四阶控制方程方面的局限性.
主要方法:
- 开发了一种结合多个神经网络的聚合模型.
- 每个网络都解决了代表特定物理 (几何,构成,平衡) 的一级或二级部分微分方程.
主要成果:
- 与经典神经网络相比,多层次PINN框架显著提高了准确性.
- 对结构力学问题的计算时间有显著的减少.
结论:
- 拟议的多级PINN框架为结构力学计算提供了显著的进步.
- 这种方法有可能在数字双胞胎系统中促进智能计算.
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