<ul><li>Data-driven emulation of nonlinear dynamics is challenging due to skill decay and unrealistic outputs.</li><li>Generative modeling with coherent priors aims to improve the quality of generated simulations.</li><li>The method presented in this work, Cohesion, unifies turbulence principles with diffusion-based modeling.</li><li>Cohesion demonstrates superior long-range forecasting skill and can generate physically-consistent simulations.</li></ul>

Generative emulation of chaotic dynamics with coherent prior

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