Generative learning for forecasting the dynamics of high-dimensional complex systems
Han Gao,
Sebastian Kaltenbach,
Petros Koumoutsakos
Abstract:We introduce generative models for accelerating simulations of high-dimensional systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instances of high dimensional data are down sampled to a lower dimensional manifold that is evolved through an auto-regressive attention mechanism. In turn, Bayesian diffusion models, that map this low-dimensional manifold onto its corresponding high-dimensional space, operate on batches of physics corr… Show more
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