论文标题
具有潜在动力学的数据驱动模型的稳定性
Stability Preserving Data-driven Models With Latent Dynamics
论文作者
论文摘要
在本文中，我们为具有潜在变量的动态问题引入了数据驱动的建模方法。所提出的模型的状态空间还包括人工潜在变量，除了可以拟合到给定数据集的观察到的变量。我们提出了一个模型框架，可以轻松执行耦合动力学的稳定性。该模型是通过复发细胞实现的，并在随着时间的推移中使用反向传播进行了训练。使用基准降低问题的基准测试的数值示例证明了模型的稳定性和复发细胞实现的效率。作为应用，考虑了两个流体结构的相互作用问题，以说明模型的准确性和预测能力。
In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables. The state-space of the proposed model includes artificial latent variables, in addition to observed variables that can be fitted to a given data set. We present a model framework where the stability of the coupled dynamics can be easily enforced. The model is implemented by recurrent cells and trained using backpropagation through time. Numerical examples using benchmark tests from order reduction problems demonstrate the stability of the model and the efficiency of the recurrent cell implementation. As applications, two fluid-structure interaction problems are considered to illustrate the accuracy and predictive capability of the model.
