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模拟集合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。该方法超越了线性依赖关系，采用互信息作为非线性依赖关系的度量指标。我们基于包含1000个成员的天气预报集合数据集（每个成员在250×352×20模拟网格上存储多个物理变量）验证了学习与重建能力。通过运行时避免计算密集型统计估计器，我们在重建主要依赖结构时显著降低了内存与计算需求。这使得估计器可嵌入至GPU加速的直接体绘制器中，实现对选定域点所有互依赖关系的交互式可视化。