Assessment of convolutional recurrent autoencoder network for learning wave propagation

TL;DR

This paper introduces CRAN, a convolutional recurrent autoencoder network, for data-driven wave propagation modeling, outperforming traditional methods in accuracy and generalization across complex physical scenarios.

Contribution

The paper presents a novel CRAN model combining convolutional autoencoders and LSTM networks for effective wave propagation learning and prediction, surpassing projection-based methods.

Findings

Achieves 90% SSIM in predictions for out-of-training cases.

Less than 10% pointwise L1 error in most cases.

Demonstrates generalization and kinematical consistency in wave patterns.

Abstract

It is challenging to construct generalized physical models of wave propagation in nature owing to their complex physics as well as widely varying environmental parameters and dynamical scales. In this article, we present the convolutional autoencoder recurrent network (CRAN) as a data-driven model for learning wave propagation phenomena. The CRAN consists of a convolutional autoencoder for learning low-dimensional system representation and a long short-term memory recurrent neural network for the system evolution in low dimension. We show that the convolutional autoencoder significantly outperforms the dimension-reduction of complex wave propagation phenomena via projection-based methods as it can directly learn subspaces resembling wave characteristics. On the other hand, the projection-based modes are restricted to the Fourier subspace. Geometric priors of the convolutional autoencoder enabling selective scale separation of complex wave dynamics further enhance its dimension-reduction capability. We also demonstrate that geometric priors such as translation equivariance and translational invariance of the convolutional autoencoder enable generalized learning of low-dimensional maps. Thus, the composite CRAN model connecting the convolutional autoencoder with a long short-term memory network specially designed for autoregressive modeling can perform generalized wave propagation prediction over the desired time horizon. Numerical experiments display 90% mean structural similarity index measure of CRAN predictions compared to true solutions for out-of-training cases, and less than 10% pointwise $L_1$ error for most cases, verifying such generalization claims. Finally, the CRAN predictions offer similar wave characteristic patterns to the target solutions indicating not only their generalization but also their kinematical consistency.