Compressed representation learning for fluid field reconstruction from sparse sensor observations

Abstract

This paper provides a new approach to reconstruct a fluid field from sparse sensor observations. Using the extreme learning machine (ELM) autoencoder, we can extract a dominant basis of the fluid field of interest from a database consisting of a series of fluid field snapshots obtained from offline computational fluid dynamics (CFD) simulations. The output weights of ELM autoencoder can be viewed as the compressed feature representations of the fluid field and represent the dominant behaviors of the database. With such a compressed representation, the fluid field of interest can be easily reconstructed from sparse sensor observations. The simulation results show that the new compressed representation approach can achieve better reconstruction accuracy as compared with the traditional principal component analysis (PCA) method.