5 papers
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald +47
The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen inte…
Federated Learning on Stochastic Neural Networks
Jingqiao Tang, Ryan Bausback, Feng Bao +1
Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data rema…
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…
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…
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…