EnKF data-driven reduced order assimilation system. (June 2022)

• Record Type: Journal Article
• Authors: Liu, C.; Fu, R.; Xiao, D.; Stefanescu, R.; Sharma, P.; Zhu, C.; Sun, S.; Wang, C.
• Is Part Of: Engineering analysis with boundary elements. Volume 139(2022)
• Page Start: 46
• Page End: 55
• Publication Date: 2022-06
• Subjects: Reduced order model -- Deep learning -- Auto-Encoder -- LSTM -- EnKF; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2022.02.016
• Languages: English
• ISSNs: 0955-7997

Abstract

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.