对于结构动态响应预测的FE减少顺序模型信息的神经操作员
Lai-Hao Yang1, Xu-Liang Luo1, Zhi-Bo Yang2
1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, PR China.
概括
基于物理学的神经网络 (PINN) 与结构动态作斗争. 一种基于里埃神经运算子 (FNO) 的新方法FRINO提供了卓越的准确性和速度,可以在各种激发下预测结构反应.
科学领域:
- 结构动力学 结构动力学
- 计算力学 计算力学 计算力学
- 机器学习 机器学习
背景情况:
- 基于物理学的神经网络 (PINN) 对微分方程有希望,但在结构动态中面临着准确性和效率的挑战.
- 直接嵌入大型结构模型作为神经网络中的约束,妨碍了可训练性和精度.
研究的目的:
- 引入一种基于福里埃神经运算子 (FNO) 的新方法FRINO,用于高精度,低成本和多功能结构动态响应预测.
- 克服PINNs在处理复杂结构动态模型方面的局限性.
主要方法:
- 采用福里埃神经运算符 (FNO) 来捕获结构动态的频域特征.
- 通过正确的直角分解集成了减少顺序模型 (ROM),以执行物理约束并降低计算成本.
- 验证了FRINO方法,使用悬臂光束在各种激发下进行动态响应预测.
主要成果:
- FRINO准确地预测结构动态反应和固有的动态特征.
- 实现了比PINNs高出两个数量级的预测准确度.
- 与PINNs相比,已经证明了高达3个数量级的计算速度提升.
- 在各种未知刺激下,FRINO 显示出广泛的灵活性来预测各种未知刺激下的反应.
结论:
- 在结构动态响应预测方面,FRINO比PINNs有显著的进步.
- 该方法提供了高精度,计算效率和多功能性.
- 最佳的FRINO性能需要仔细考虑物理损失,数据分辨率和网络架构.
相关概念视频
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01:26Linear Approximation in Frequency Domain
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01:19Second-order Op Amp Circuits
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01:20Second Order systems I
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