论文标题
用神经网络重采样多尺度系统中的随机参数化
Resampling with neural networks for stochastic parameterization in multiscale systems
论文作者
论文摘要
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。考虑到未解决的过程的效果很重要，这引入了参数性的需求。我们提出了一种机器学习方法，用于从完全解析的模拟中进行观测值或参考数据的条件重新采样。它基于以宏观变量为条件的参考数据子集的概率分类。该方法用于制定一种随机化的参数化，考虑到未解决的尺度的不确定性。我们使用两个不同的参数设置来验证Lorenz 96系统上的方法，这对于参数化方法具有挑战性。
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.
