Oct. 27, 2022
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Project co-led at LLNL looks to improve visualization of largescale datasets

Jeremy Thomas/LLNL

LLNL researchers are starting work on a three-year project aimed at improving methods for visual analysis of large heterogeneous data sets as part of a recent Department of Energy funding opportunity. The joint project, entitled “Neural Field Processing for Visual Analysis,” will be led at LLNL by co-principal investigator (PI) Andrew Gillette. Gillette is joined by lead PI Matthew Berger at Vanderbilt University and co-PI Joshua Levine at the University of Arizona. The newly funded project will explore methods for processing “implicit neural representations” (INRs) — datasets that incorporate coordinate-based neural networks to represent scientific data sets efficiently and compactly. Currently, traditional processing algorithms and visual analysis techniques cannot be applied to INRs directly, Gillette explained. “It’s an honor to have been selected to carry out this research for the DOE,” Gillette said. “Fast and accurate visualization is essential for a wide variety of activities underway at DOE laboratories; my goal over the next three years is to partner closely with application domain specialists and demonstrate how advances in visualization methodologies can directly benefit scientific inquiry.” Read more at LLNL News.