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Probabilistic predictions of SIS epidemics on networks based on population-level observations
To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model.
Downscaling near-surface atmospheric fields with multi-objective Genetic Programming
We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn equations or short programs that reconstruct the fine-scale fields (e. g., 400 m resolution) of the near-surface atmospheric state variables from the coarse atmospheric model output (e. g., 2. 8 km resolution).
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