AI RESEARCH
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
arXiv CS.AI
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ArXi:2603.05560v1 Announce Type: cross 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) system. By projecting nonlinear dynamics into a latent space governed by a linear ordinary differential equation, the model enforces structured and interpretable temporal evolution while enabling temporally resolution-invariant forecasting via a matrix exponential formulation.