Bio
Ming Fan is a research scientist in the Computational Sciences and Engineering Division at 91°µÍø (ORNL). His research focuses on the intersection of machine learning, surrogate modeling, inverse modeling, uncertainty quantification, and high-performance computing. Ming collaborates with interdisciplinary teams of Earth and computational scientists to develop novel AI/ML methodologies, advancing the field of Earth sciences.
Publications
March 2025
Journal: Journal of Hydrometeorology
February 2025
A novel conditional generative model for efficient ensemble forecasts of state variables in large…
Journal: Journal of Hydrology
February 2025
Conference Paper
November 2024
Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore…
Journal: SPE Journal
November 2024
Journal: The International Conference for High Performance Computing, Networking, Storage, and Analysis