EnKF data-driven reduced order assimilation system

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.

Automated summary

This work presents a new predictive data assimilation framework based on a data-driven reduced order model (DDROM), and demonstrates its capabilities through two test cases.