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

Autor(en)

Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann

Abstrakt

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(en)

Institut für Meteorologie und Geophysik

Externe Organisation(en)

Technische Universität München, RIKEN

DOI

https://doi.org/10.2312/vmv.20231229

Publikationsdatum

2023

Peer-reviewed

Ja

ÖFOS 2012

105206 Meteorologie

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/neural-fields-for-interactive-visualization-of-statistical-dependencies-in-3d-simulation-ensembles(45a3022d-7ad8-4214-a3b1-f246690d3f07).html