EnKF data-driven reduced order assimilation system

doi:10.1016/j.enganabound.2022.02.016

EnKF data-driven reduced order assimilation system

2022-06-01

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AI

DOI：10.1016/j.enganabound.2022.02.016

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Abstract

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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.

Keywords:

Reduced order model

Deep learning

Auto-Encoder

LSTM

EnKF

EnKF data-driven reduced order assimilation system

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