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大型3D模拟系综中不同空间位置物理变量之间统计依赖关系，并高效重建该关系的神经网络。该方法超越了线性依赖的局限，采用互信息作为非线性依赖的度量标准。我们在包含1000个成员的大型天气预报系综上进行了学习与重建实验，每个成员在250×352×20的模拟网格中存储多个物理变量。通过避免在运行时使用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内存与计算需求。这使得该估计器能够嵌入至GPU加速的直接体绘制器中，并实现对选定域点所有互依赖关系的交互式可视化。