Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles
Fatemeh Farokhmanesh1, Kevin Höhlein1, Christoph Neuhauser1, Tobias Necker2, Martin Weissmann2, Takemasa Miyoshi3, Rüdiger Westermann1
1 Chair of Computer Graphics and Visualization, Technical University of Munich, Germany
2 Department of Meteorology and Geophysics, University of Vienna, Austria
3 RIKEN Center for Computational Science, Kobe, Japan
Abstract
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 × 352 × 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.
Associated publications
Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles
Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
Vision, Modeling, and Visualization 2023 (VMV 2023)
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