35 citations · 47 across the 6 of their papers we have counts for
7 papers
An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials
Rinkle Juneja, Viktor Reshniak, Richard K. Archibald +4
Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and multiresolution character of mod…
MPEX AI Digital Twins Milestone Report
Gary Staebler, Rhea Barnett, Mark Cianciosa +13
This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. Th…
A General Framework for Error-controlled Unstructured Scientific Data Compression
Qian Gong, Zhe Wang, Viktor Reshniak +10
Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence…
A framework for compressing unstructured scientific data via serialization
Viktor Reshniak, Qian Gong, Rick Archibald +2
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite eleme…
Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order
Viktor Reshniak, Evan Ferguson, Qian Gong +3
MGARD (MultiGrid Adaptive Reduction of Data) is an algorithm for compressing and refactoring scientific data, based on the theory of multigrid methods. The core algorithm is built…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Qian Gong, Chengzhu Zhang, Xin Liang +10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated…