8 citations · 10 across the 2 of their papers we have counts for
5 papers
A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics
Junqi Yin, Siming Liang, Siyan Liu +4
The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weathe…
Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data
Viktor Reshniak, Xiaoping Wang, Guannan Zhang +2
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected se…
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…
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…