Interpretable reduced-order modeling with time-scale separation
This work addresses computational challenges in physics and engineering by providing an interpretable and stable modeling approach, though it appears incremental as it builds on existing autoencoder and latent dynamics methods.
The authors tackled the computational expense of solving high-dimensional PDEs by proposing a data-driven reduced-order modeling scheme that automatically identifies time-scales and ensures stable predictions under varied conditions, demonstrating applicability in systems like the Kuramoto-Shivashinsky equation and improving results with a probabilistic version.
Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering. However, finding solutions for these PDEs can be computationally expensive, making model-order reduction crucial. We propose such a data-driven scheme that automates the identification of the time-scales involved and can produce stable predictions forward in time as well as under different initial conditions not included in the training data. To this end, we combine a non-linear autoencoder architecture with a time-continuous model for the latent dynamics in the complex space. It readily allows for the inclusion of sparse and irregularly sampled training data. The learned, latent dynamics are interpretable and reveal the different temporal scales involved. We show that this data-driven scheme can automatically learn the independent processes that decompose a system of linear ODEs along the eigenvectors of the system's matrix. Apart from this, we demonstrate the applicability of the proposed framework in a hidden Markov Model and the (discretized) Kuramoto-Shivashinsky (KS) equation. Additionally, we propose a probabilistic version, which captures predictive uncertainties and further improves upon the results of the deterministic framework.