  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.
