用深度学习加速多体系统动态响应模拟，可实时预测且无需完整状态
SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems
- 基于滑动窗口与初始截断，利用阻尼特性压缩输出序列
- 对不同系统提速达数百万倍，超越实时性能
- 适合需要高速仿真的柔性多体系统设计与控制
在计算工程中，提升仿真速度与效率是持续目标。为充分借助神经网络技术和硬件能力，我们提出一种基于深度学习的方法——滑动窗口初始截断动态响应估计算法（SLIDE），用于估计具有主要但非仅限于受迫激励的机械或多体系统的输出序列。其关键优势在于无需完整系统状态即可估算阻尼系统的动态响应，特别适用于柔性多体系统。该方法根据初始效应（如阻尼）衰减情况截断输出窗口，其衰减速率通过系统线性化方程的复特征值近似。此外，额外训练第二个神经网络以提供误差估计，进一步增强方法适用性。该方法应用于多种系统：杜芬振子、柔性滑块曲柄机构及安装于柔性基座的工业6R机械臂。结果表明，仿真速度提升达数百万倍，显著超过实时性能。
原文摘要 · Abstract (English)
In computational engineering, enhancing the simulation speed and efficiency is a perpetual goal. To fully take advantage of neural network techniques and hardware, we present the SLiding-window Initially-truncated Dynamic-response Estimator (SLIDE), a deep learning-based method designed to estimate output sequences of mechanical or multibody systems with primarily, but not exclusively, forced excitation. A key advantage of SLIDE is its ability to estimate the dynamic response of damped systems without requiring the full system state, making it particularly effective for flexible multibody systems. The method truncates the output window based on the decay of initial effects, such as damping, which is approximated by the complex eigenvalues of the systems linearized equations. In addition, a second neural network is trained to provide an error estimation, further enhancing the methods applicability. The method is applied to a diverse selection of systems, including the Duffing oscillator, a flexible slider-crank system, and an industrial 6R manipulator, mounted on a flexible socket. Our results demonstrate significant speedups from the simulation up to several millions, exceeding real-time performance substantially.
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