DynNet：用于线性和非线性结构响应建模和预测的基于物理的神经结构设计
DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction
摘要：
数据驱动模型预测线性和非线性系统的动态响应是非常重要的，因为它们广泛应用于从概率分析到反问题，如系统辨识和损伤诊断。在这项研究中，一个基于物理的递归神经网络模型的设计，是能够学习的线性和非线性多自由度系统的动力学地面运动。该模型能够估计一套完整的响应，包括位移，速度，加速度和内力。与最先进的模型相比，该模型需要更少的可训练变量，而对于长轨迹的预测精度更高。此外，经常性的块的架构的启发，微分方程求解器算法，预计这种方法会产生更广义的解决方案。在训练阶段，我们提出了多种新的技术，以显着加快学习过程中使用较小的数据集，如硬采样，利用轨迹损失函数，并实施信任区域的方法。数值案例研究进行检查的强度的网络学习不同的非线性行为。结果表明，该网络能够捕获动态系统的不同非线性行为，具有非常高的精度，不需要先验信息或非常大的数据集。
英文摘要：
Data-driven models for predicting dynamic responses of linear and nonlinear systems are of great importance due to their wide application from probabilistic analysis to inverse problems such as system identification and damage diagnosis. In this study, a physics-based recurrent neural network model is designed that is able to learn the dynamics of linear and nonlinear multiple degrees of freedom systems given a ground motion. The model is able to estimate a complete set of responses, including displacement, velocity, acceleration, and internal forces. Compared to the most advanced counterparts, this model requires a smaller number of trainable variables while the accuracy of predictions is higher for long trajectories. In addition, the architecture of the recurrent block is inspired by differential equation solver algorithms and it is expected that this approach yields more generalized solutions. In the training phase, we propose multiple novel techniques to dramatically accelerate the learning process using smaller datasets, such as hardsampling, utilization of trajectory loss function, and implementation of a trust-region approach. Numerical case studies are conducted to examine the strength of the network to learn different nonlinear behaviors. It is shown that the network is able to capture different nonlinear behaviors of dynamic systems with very high accuracy and with no need for prior information or very large datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
