2 papers
cs.LG2026
Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting
Rares Grozavescu, Pengyu Zhang, Etienne Meunier +1
Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and free of fixed-timestep assumpti…
cs.LG2026
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
Rares Grozavescu, Pengyu Zhang, Mark Girolami +1
We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer quasi-geostrophic (QG) syst…