MetaKoopman is a Bayesian meta-learning method for the Koopman operator that enables fast, uncertainty-aware prediction and planning for nonlinear dynamics under distribution shifts.

We propose the Koopman Distillation Model (KDM), a novel offline distillation method for diffusion models that leverages Koopman theory to enable single-step generation with strong semantic consistency and state-of-the-art FID performance.