Publications
Showing 20 results for Author: Viktor Reshniak
Mar, 2026
Conference Paper
Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control
In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global me…
Mar, 2026
Conference Paper
A Framework for Compressing Unstructured Scientific Data via Serialization
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process d…
Mar, 2026
Conference Paper
Assessing Membership Inference Attacks under Distribution Shifts
Membership inference attacks (MIAs) exploit machine learning models to infer whether a data point was in the training set, posing significant privacy risks even with limited black-box access. These attacks rely on the attacker approximating the target model’s training distribution, yet the impact of distribution shifts between target and shadow models on MIA success remain…
Mar, 2026
Conference Paper
A General Framework for Error-controlled Unstructured Scientific Data Compression
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 in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstru…
Mar, 2026
Conference Paper
Evaluation of Leak Detection Technologies for Low Global Warming Potential (GWP), Flammable Refrigerants
Current commercial refrigeration systems use refrigerants with global warming potential (GWP) values ranging from 1250 to 4000. The emergence of low GWP alternatives (GWP
Jan, 2026
Book Chapter
The Security of Reinforcement Learning Systems in Electric Grid Domain
Over the past few years, reinforcement learning (RL) has seen wide-scale applicability and possible deployments within power grids. Some of the applications of RL include control, cybersecurity, privacy, optimization, and energy management. The main advantage that RL offers within these areas is its ability to deal with unknown uncertainties via dynamic, experiential learn…
Dec, 2024
Journal
Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional…
Dec, 2024
Journal
Lifting MGARD: Construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order
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 around stable multilevel decompositions of conforming piecewise linear $C^0$ finite element spaces, enabling accurate error control in various norms and derived quantities of interest. In this wo…
Jan, 2024
Conference Paper
Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis
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 over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptiv…
Jan, 2024
Journal
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requirements, including storage reduction, high-performance I/O, and in-situ data analysis. It features a unified application pro…