State estimation with limited sensors -- A deep learning based approach

Understand

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.