8 citations · 11 across the 5 of their papers we have counts for
5 papers
HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction
Pengfei Hu, Fan Ming, Xiaoxue Han +3
Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differen…
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Pengfei Hu, Ming Fan, Xiaoxue Han +5
Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interco…
GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
Ming Fan, Zezhong Zhang, Dan Lu +1
We introduce GenAI4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. Ge…
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
Minglei Yang, Pengjun Wang, Ming Fan +3
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and i…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…