The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning. - Wai Tong Chung; Ki Sung Jung; Jacqueline H. Chen; Matthias Ihme (CoRR, 2022)

Real-fluid thermophysicalModels: An OpenFOAM-based library for reacting flow simulations at high pressure. - Danh Nam Nguyen; Ki Sung Jung; Jae Won Shim 003; Chun Sang Yoo (Comput. Phys. Commun., 2022)

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. - Wai Tong Chung; Bassem Akoush; Pushan Sharma; Alex Tamkin; Ki Sung Jung; Jacqueline Chen; Jack Guo; Davy Brouzet; Mohsen Talei; Bruno Savard; Alexei Y. Poludnenko; Matthias Ihme (NeurIPS, 2023)

Transfer learning for predicting source terms of principal component transport in chemically reactive flow. - Ki Sung Jung; Tarek Echekki; Jacqueline H. Chen; Mohammad Khalil (CoRR, 2023)

Machine Learning Techniques for Data Reduction of CFD Applications. - Jaemoon Lee; Ki Sung Jung; Qian Gong; Xiao Li; Scott Klasky; Jacqueline Chen; Anand Rangarajan 001; Sanjay Ranka (CoRR, 2024)

Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows. - Bruno S. Soriano; Ki Sung Jung; Tarek Echekki; Jacqueline H. Chen; Mohammad Khalil (CoRR, 2024)

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis. - Weiming He; Qi Chen; Qian Gong; Jing Li; Qing Liu 002; Norbert Podhorszki; Scott Klasky; Ki Sung Jung; Cristian Lacey; Jackie Chen; Hongjian Zhu (ICDE, 2025)