Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
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Spatiotemporal chaos in systems such as turbulent flows often resides on finite-dimensional attractors, enabling the construction of reduced-order models. Unfortunately, as the dimension of these attractors increases it becomes more difficult to train reduced-... ...
