The importance of state estimation in fluid mechanics is well-established; it is required for accomplishing several tasks including design/optimization, active control, and future state prediction. A common tactic in this regards is to rely on reduced order models. Such approaches, in general, use measurement data of one-time instance. However, oftentimes data available from sensors is sequential and ignoring it results in information loss. In this paper, we propose a novel deep learning based state estimation framework that learns from sequential data. The proposed model structure consists of the recurrent cell to pass information from different time steps enabling utilization of this information to recover the full state. We illustrate that utilizing sequential data allows for state recovery from only one or two sensors. For efficient recovery of the state, the proposed approached is coupled with an auto-encoder based reduced order model. We illustrate the performance of the proposed approach using two examples and it is found to outperform other alternatives existing in the literature.
翻译:在流体力学中,国家估算的重要性已经确立;完成若干任务,包括设计/优化、积极控制和未来状态预测,都需要国家估算的重要性。这方面的一个常见策略是依赖减少顺序模型。一般而言,这类方法使用一次性的测量数据。然而,传感器提供的数据往往按顺序排列,忽视它造成信息损失。在本文件中,我们提出了一个从顺序数据中学习的新颖的深层次基于深层次学习的国家估算框架。拟议的模型结构包括从不同时间步骤传递信息的经常性单元,以便利用这一信息恢复整个状态。我们说明,利用顺序数据只能从一个或两个传感器中恢复状态。为有效恢复状态,拟议的方法与基于自动编码的减少顺序模型相结合。我们用两个例子来说明拟议方法的绩效,并发现它比文献中现有的其他替代方法更完善。