论文标题
来自小数据集的极端事件的统计预测
Statistical prediction of extreme events from small datasets
论文作者
论文摘要
我们建议回声状态网络（ESN）预测湍流中极端事件的统计数据。我们在小型数据集上训练ESN，这些数据集缺乏有关极端事件的信息。我们可以评估网络是否能够从小型不完美数据集中推断并预测描述事件的重尾统计信息。我们发现，在几乎所有分析的情况下，网络正确预测事件并改善了系统的统计数据。这为湍流中极端事件的统计预测打开了新的可能性。
We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme events. We asses whether the networks are able to extrapolate from the small imperfect datasets and predict the heavy-tail statistics that describe the events. We find that the networks correctly predict the events and improve the statistics of the system with respect to the training data in almost all cases analysed. This opens up new possibilities for the statistical prediction of extreme events in turbulence.
