8 citations · 13 across the 7 of their papers we have counts for
7 papers
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…
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier
Aristeidis Tsaris, Chengming Zhang, Xiao Wang +9
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence length…
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability
Xiao Wang, Siyan Liu, Aristeidis Tsaris +8
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by le…
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…
Rényi Divergence Deep Mutual Learning
Weipeng Huang, Junjie Tao, Changbo Deng +4
This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm. We propose using Rényi divergence instead of the KL divergence, which is more flexible an…
A Spatiotemporal-Aware Climate Model Ensembling Method for Improving Precipitation Predictability
Ming Fan, Dan Lu, Deeksha Rastogi +1
Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian…