35 citations · 49 across the 15 of their papers we have counts for
8 papers · 1 filter
Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest
Xuan Wu, Qian Gong, Jieyang Chen +4
The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigat…
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…
Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models
Tushar M. Athawale, Zhe Wang, David Pugmire +5
This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fund…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…