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.
翻译:在本文中,我们为潜在变量的动态问题采用了数据驱动模型方法;拟议模型的状态空间包括人工潜伏变量,以及可与某一数据集相适应的观测到变量;我们提出了一个模型框架,可以很容易地实现组合动态的稳定性;该模型由经常性细胞实施,并经过培训,通过时间反向调整;使用减少顺序问题基准测试的数值实例表明模型的稳定性和经常性单元格实施的效率;作为应用,可以考虑两种流体结构互动问题,以说明模型的准确性和预测能力。