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

Veröffentlichungen: Beitrag in BuchBeitrag in KonferenzbandPeer Reviewed

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.
OriginalspracheEnglisch
TitelVMV 2023: Vision, Modeling, and Visualization
ISBN (elektronisch)978-3-03868-232-5
DOIs
PublikationsstatusVeröffentlicht - 2023
VeranstaltungVMV 2023, Vision, Modeling, and Visualization. The Eurographics Association - , Deutschland
Dauer: 27 Sept. 202329 Sept. 2023

Konferenz

KonferenzVMV 2023, Vision, Modeling, and Visualization. The Eurographics Association
Land/GebietDeutschland
Zeitraum27/09/2329/09/23

ÖFOS 2012

  • 105206 Meteorologie

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