Jong Youl Choi Contact choij@ornl.gov | 865.241.1436 All Publications First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training Role of turbulent separatrix tangle in the improvement of the integrated pedestal and heat exhaust issue for stationary-operation tokamak fusion reactors Active learning of neural network potentials for rare events Fast Algorithms for Scientific Data Compression User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets Analyzing File Access Patterns on Large-Scale HPC Systems: Opportunities for File Prefetching Online and Scalable Data Compression Pipeline with Guarantees on Quantities of Interest... Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets An Algorithmic and Software Pipeline for Very Large Scale Scientific Data Compression with Error Guarantees A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions... Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules... Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities... Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems... A codesign framework for online data analysis and reduction Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems... Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression... Co-design Center for Exascale Machine Learning Technologies (ExaLearn)... A codesign framework for online data analysis and reduction DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers... Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs... The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science... Pagination Current page 1 Page 2 Page 3 Next page ›› Last page Last » Key Links ORCID Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Mathematics in Computation Section Discrete Algorithms Group
