This paper presents a new approach to estimate one physical field from off-line computational fluid dynamics (CFD) database and real-time sparse sensor observations. Firstly, we determine the proper orthogonal decomposition (POD) modes from the CFD database. Then, we use extreme learning machine (ELM) to build a regression model between the boundary conditions of physical fields and their POD coefficients. With this model, we can directly estimate the physical field of interest. Next, we modify the estimated physical field based on sparse sensor observations with the help of the dominant POD modes. The modified physical field is shown more accurate than the physical field estimated from either the regression model or sensor observations. Finally, we provide a simple example to show the effectiveness of the proposed approach.
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