In simulations of multiscale dynamical systems, not all relevant processes can be resolved explicitly. Taking the effect of the unresolved processes into account is important, which introduces the need for paramerizations. We present a machine-learning method, used for the conditional resampling of observations or reference data from a fully resolved simulation. It is based on the probabilistic classiffcation of subsets of reference data, conditioned on macroscopic variables. This method is used to formulate a parameterization that is stochastic, taking the uncertainty of the unresolved scales into account. We validate our approach on the Lorenz 96 system, using two different parameter settings which are challenging for parameterization methods.

中文翻译：

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在多尺度系统中使用神经网络重新采样以进行随机参数化

在多尺度动力系统的模拟中，并非所有相关过程都可以明确解决。考虑未解决过程的影响很重要，这引入了对参数化的需求。我们提出了一种机器学习方法，用于从完全解析的模拟中对观察或参考数据进行有条件的重采样。它基于参考数据子集的概率分类，以宏观变量为条件。该方法用于制定随机参数化，将未解析尺度的不确定性考虑在内。我们在 Lorenz 96 系统上验证了我们的方法，使用对参数化方法具有挑战性的两种不同参数设置。