Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

TL;DR

This paper introduces a lightweight, interpretable convolutional autoencoder for high-dimensional fluid flow data that improves reconstruction accuracy over traditional methods while providing meaningful features, addressing the black-box nature of standard CAEs.

Contribution

The authors propose a novel interpretable CAE method that maintains high reconstruction quality, enhances interpretability of features, and is resource-efficient compared to existing approaches.

Findings

Achieves up to 6.4% better reconstruction than POD with 64 modes.

Provides interpretable features comparable to POD.

Uses less than 2% of parameters of existing CAEs.

Abstract

In recent years, data-driven deep learning models have gained significant interest in the analysis of turbulent dynamical systems. Within the context of reduced-order models (ROMs), convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. They can learn nonlinear transformations directly from data, without prior knowledge of the system. However, the features generated by such models lack interpretability. Thus, the resulting model is a black-box which effectively reduces the complexity of the system, but does not provide insights into the meaning of the latent features. To address this critical issue, we introduce a novel interpretable CAE approach for high-dimensional fluid flow data that maintains the reconstruction quality of conventional CAEs and allows for feature interpretation. Our method can be easily integrated into any existing CAE architecture with minor modifications of the training process. We compare our approach to Proper Orthogonal Decomposition (POD) and two existing methods for interpretable CAEs. We apply all methods to three different experimental turbulent Rayleigh-B\'enard convection datasets with varying complexity. Our results show that the proposed method is lightweight, easy to train, and achieves relative reconstruction performance improvements of up to 6.4% over POD for 64 modes. The relative improvement increases to up to 229.8% as the number of modes decreases. Additionally, our method delivers interpretable features similar to those of POD and is significantly less resource-intensive than existing CAE approaches, using less than 2% of the parameters. These approaches either trade interpretability for reconstruction performance or only provide interpretability to a limited extend.