Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks

TL;DR

This paper introduces CNN-enhanced multi-fidelity Ensemble Kalman Filter algorithms that improve prediction accuracy and convergence speed in flow data assimilation, reducing computational costs.

Contribution

It develops CNN-augmented multi-fidelity algorithms for the Ensemble Kalman Filter, enhancing prediction accuracy and efficiency in flow data assimilation tasks.

Findings

CNN tools significantly improve low-fidelity model accuracy

Faster convergence of data assimilation algorithms observed

Reduced computational costs achieved

Abstract

The present research work proposes advancement for Data Assimilation strategies using Convolutional Neural Networks (CNN). More precisely, multi-fidelity and multi-level algorithms for the Ensemble Kalman Filter are enhanced by CNN tools, with the objective to reduce the discrepancy in the prediction between ensemble realizations performed with different models. The proposed methodology is assessed via the analysis of the flow through a cascade of NACA 0012 profiles for Reynolds $Re=1\,000$ and Mach $Ma=0.5$. Depending on the angle of attack $\alpha$, unsteady features of the flow can be observed. The results indicate that the usage of the CNN tools, which are trained using data from the DA procedure, significantly augments the accuracy of the low-fidelity models with little augmentation in computational costs. It is shown that the usage of the CNN tools provides a faster convergence of the Data Assimilation algorithms, which leads to a significant gain in terms of computational resources required.