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We present the first neural network that has learned to compactly representand can efficiently reconstruct the statistical dependencies between the valuesof physical variables at different spatial locations in large 3D simulationensembles. Going beyond linear dependencies, we consider mutual information asa measure of non-linear dependence. We demonstrate learning and reconstructionwith a large weather forecast ensemble comprising 1000 members, each storingmultiple physical variables at a 250 x 352 x 20 simulation grid. Bycircumventing compute-intensive statistical estimators at runtime, wedemonstrate significantly reduced memory and computation requirements forreconstructing the major dependence structures. This enables embedding theestimator into a GPU-accelerated direct volume renderer and interactivelyvisualizing all mutual dependencies for a selected domain point.

Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles