Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting

ArXi:2602.02832v2 Announce Type: replace Learning surrogate models for time-dependent PDEs requires balancing expressivity, stability, and computational efficiency. While highly expressive generative models achieve strong short-term accuracy, they rely on autoregressive sampling procedures that are computationally expensive and prone to error accumulation over long horizons. We propose a continuous-time Koopman autoencoder in which latent dynamics are governed by a parameter-conditioned linear generator.