Data repairing and resolution enhancement using data-driven modal decomposition and deep learning

TL;DR

This paper presents innovative data-driven methods combining modal decomposition and deep learning to repair, enhance, and accurately reconstruct complex, noisy datasets across numerical and experimental data types.

Contribution

It introduces a novel combination of modal decomposition algorithms with deep learning architectures for data repair and resolution enhancement.

Findings

Effective reconstruction of complex, noisy datasets

Outperforms traditional methods in data enhancement

Applicable to both numerical and experimental data

Abstract

This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and deep learning architectures to repair, enhance, and increase the quality and precision of numerical and experimental data. A combination of two- and three-dimensional, numerical and experimental dasasets are used to demonstrate the reconstruction capacity of the presented methods, showing that these methods can be used to reconstruct any type of dataset, showing outstanding results when applied to highly complex data, which is noisy. The combination of benefits of these techniques results in a series of data-driven methods which are capable of repairing and/or enhancing the resolution of a dataset by identifying the underlying physics that define the data, which is incomplete or under-resolved, filtering any existing noise. These methods and the Python codes are included in the first release of ModelFLOWs-app.