EnKF data-driven reduced order assimilation system. (June 2022) - Liu, C.; Fu, R.; Xiao, D.; Stefanescu, R.; Sharma, P.; Zhu, C.; Sun, S.; Wang, C.
Abstract: This work presents a new predictive data assimilation framework based on a data-driven reduced order model (DDROM). The DDROM is constructed using an Auto-Encoder and a long short-term memory (LSTM) neural networks. The Auto-Encoder is used to project the high-dimensional dynamics into a lower-dimensional space, which can be referred as a latent space. Then, LSTM deep learning method is used to construct a number of response functions to represent the fluid states and dynamics in the latent space. A data assimilation framework based on the Ensemble Kalman Filter (EnKF) and DDROM model is then proposed. A demonstration of the capabilities of this data assimilation system is illustrated by two test cases including the 2D Burgers' equation and the flow past a cylinder governed by Navier–Stokes equations.