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Poster
in
Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model

Fran├žois Rozet · Gilles Louppe


Abstract:

Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challenges due to the high-dimensionality of geophysical dynamical systems, often exceeding millions of dimensions. This work assesses the scalability of score-based data assimilation (SDA), a novel data assimilation method, in the context of such systems. We propose modifications to the score network architecture aimed at significantly reducing memory consumption and execution time. We demonstrate promising results for a two-layer quasi-geostrophic model.

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