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.

本研究解决了计算工程中仿真速度和效率提升的需求，提出了一种深度学习方法SLIDE，用于估计机械或多体系统的输出序列。SLIDE的创新在于无需完整系统状态即可有效估计阻尼系统的动态响应，显著提高了灵活多体系统的仿真性能，实验结果表明该方法在多个系统中实现了显著的加速，远超实时性能。

SLIDE：一种基于机器学习的多体系统强迫动态响应估计方法