恭喜蒋俊杰博士关于利用深度卷积神经网络预测Extreme event的文章发表。具体文章标题与摘要如下：

Predicting extreme events from data using deep machine learning: When and where

      We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

全文链接：https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.023028

摘要翻译：

针对基于二维时空动力学系统的测量或观测数据预测系统中极端事件何时何地发生的难题，我们开发了一个基于深度卷积神经网络的预测框架。我们通过使用适当的对二维时空系统的快照图像的标记方案，成功的训练了深度卷积神经网络，并完成了对系统中将要发生的极端事件的预测。另一方面，如果在已知极端事件将发生的情况下，我们基于空间标记的方法，可以在一定空间分辨率的情况下预测极端事件将发生的位置。我们使用了二维复Ginzburg-Landau 方程的数据和来自北大西洋的真实风速数据测试了我们的预测框架，并且探讨了该框架下预测范围、空间分辨率和准确性之间的权衡，讨论了极端事件的空间分布偏差对预测准确性的不利影响。该深度学习框架可用于预测真实世界中的极端灾害事件。

该文章的其它作者包含，西安交通大学类脑智能研究中心主任黄子罡教授、欧洲科学院院士来颖诚教授和Celso Grebogi教授。