Jong Youl Choi Contact 865.241.1436 | CHOIJ@ORNL.GOV All Publications Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability... MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training... Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations 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 DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks 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) DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers Pagination Current page 1 Page 2 Page 3 Next page ›â¶Äº Last page Last » Key Links Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Mathematics in Computation Section Discrete Algorithms Group
