Ming Fan Research Scientist Contact FANM@ORNL.GOV All Publications An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage Derivation of A Representative Elementary Volume (REV) for Upscaled Two-Phase Flow in Porous Media... Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability... A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method... Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods A deep learning-based direct forecasting of CO2 plume migration... A Spatiotemporal-Aware Weighting Scheme for Improving Climate Model Ensemble Predictions Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods... Relative permeability as a stationary process: Energy fluctuations in immiscible displacement Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks Multimodel ensemble predictions of precipitation using bayesian neural networks Key Links Google Scholar ORCID LinkedIn GitHub Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Physical Sciences Section Computational Earth Sciences Group
