We present the first neural network that has learned to compactly represent
and can efficiently reconstruct the statistical dependencies between the values
of physical variables at different spatial locations in large 3D simulation
ensembles. Going beyond linear dependencies, we consider mutual information as
a measure of non-linear dependence. We demonstrate learning and reconstruction
with a large weather forecast ensemble comprising 1000 members, each storing
multiple physical variables at a 250 x 352 x 20 simulation grid. By
circumventing compute-intensive statistical estimators at runtime, we
demonstrate significantly reduced memory and computation requirements for
reconstructing the major dependence structures. This enables embedding the
estimator into a GPU-accelerated direct volume renderer and interactively
visualizing all mutual dependencies for a selected domain point.

本研究提出了首个神经网络，该网络已学会紧凑地表示并能够有效地重构大型三维模拟集合中不同空间位置上物理变量之间的统计依赖关系，其中对于非线性依赖关系采用了互信息进行测量，并通过大型天气预报集合进行了学习和重构。