Enhancing Future Prediction of Linear and Nonlinear Reduced-Order Models for Transport-Dominated Problems Using Lagrangian Data
This work addresses the problem of poor future prediction in ROMs for transport-dominated systems, which is incremental by leveraging Lagrangian data to enhance existing autoencoder and dynamic mode decomposition methods.
The paper tackled the challenge of accurately predicting future solutions in transport-dominated problems using reduced-order models (ROMs) by shifting from Eulerian to Lagrangian frames, which improved coherence and reduced the Kolmogorov n-width decay. The result was that Lagrangian-based ROMs achieved more accurate and stable future predictions compared to Eulerian counterparts.
Designing effective reduced-order models (ROMs) for parametrized transport-dominated problems remains challenging because of the well-known Kolmogorov barrier. Autoencoder-based nonlinear ROMs have been developed to improve the compression ability for such systems. However, despite their stronger compression ability, autoencoder-based ROMs constructed in the Eulerian frame may fail to accurately predict future solutions, due to the poor coherence between historical and future solutions in the Eulerian frame. In contrast, we show that representing transport-dominated dynamics in the Lagrangian frame can lead to a significantly faster decay of the Kolmogorov n-width and improve coherence between historical and future solutions. Building on these insights, we develop two non-intrusive ROMs leveraging Lagrangian data: a Lagrangian autoencoder-based ROM and a Lagrangian parametric dynamic mode decomposition. Numerical experiments demonstrate that these Lagrangian ROMs achieve more accurate and stable future predictions than their Eulerian counterparts.