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：我们提出了首个能够紧凑表示并高效重建大规模3D模拟集成中不同空间位置物理变量值之间统计依赖关系的神经网络。超越线性依赖范畴，我们采用互信息作为非线性依赖的度量。我们基于包含1000个成员的大型天气预报集成系统（每个成员在250×352×20的模拟网格上存储多个物理变量）展示了学习与重建过程。通过规避运行时计算密集型的统计估计器，我们显著降低了重建主要依赖结构所需的内存与计算资源。这一方法使得统计估计器能嵌入GPU加速的直接体渲染器，并对所选领域点的所有互依赖关系进行交互式可视化。