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

TL;DR

This paper introduces a deep learning framework utilizing recurrent neural networks and auto-encoders for efficient state estimation in fluid mechanics from limited sequential sensor data, outperforming existing methods.

Contribution

It presents a novel deep learning approach that leverages sequential data and reduced order models for accurate state estimation with minimal sensors.

Findings

State recovery from only one or two sensors is possible.

The proposed method outperforms existing state estimation techniques.

Sequential data utilization improves estimation accuracy.

Abstract

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.