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.

翻译：我们提出了首个神经网络，该网络能够紧凑地表示并高效重建大型3D模拟集合中不同空间位置的物理变量值之间的统计依赖关系。超越线性依赖，我们采用互信息作为非线性依赖的度量。我们利用一个包含1000个成员的大型天气预报集合进行学习和重建演示，每个成员在250×352×20的模拟网格上存储多个物理变量。通过绕过运行时计算密集的统计估计器，我们显著降低了重建主要依赖结构所需的存储和计算要求。这使得能够将估计器嵌入到GPU加速的直接体绘制器中，并交互式地可视化选定域点的所有互依赖关系。