Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles
- Author(s)
- Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
- Abstract
We present neural dependence fields (NDFs) - the first neural network that learns to compactly represent and 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 an exemplary 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.
- Organisation(s)
- Department of Meteorology and Geophysics
- External organisation(s)
- Technische Universität München, Ulmer Fundamental Symmetries Laboratory
- DOI
- https://doi.org/10.2312/vmv.20231229
- Publication date
- 2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 105206 Meteorology
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/neural-fields-for-interactive-visualization-of-statistical-dependencies-in-3d-simulation-ensembles(45a3022d-7ad8-4214-a3b1-f246690d3f07).html