Yuan's NASA AIST Proposal Selected for Funding

Tianle Yuan (613/UMBC) is the PI on the recently awarded NASA AIST proposal, “Building Earth System Digital Twins with Deep Generative Models for Improved Simulation of Clouds and Their Feedbacks and Impacts.” The research team includes Co-Is Yue Dong (UCLA), Jia-Bin Huang (UMD), Hua Song (613/SSAI), and Lazaros Oreopoulos (613/GSFC). The grant is funded for three years, with an expected start date in January 2025.

Dr. Yuan explained that the team “will be developing an Earth Science Digital Twins (ESDT) component with Artificial Intelligence – Machine Learning (AI-ML) techniques to address a host of research topics.”  From the proposal, he states, “We propose to develop deep generative models (DGMs) to serve as ESDT components to improve the simulations of clouds and cloud feedbacks to climate change while drastically reducing the computational cost.” Additionally, “The technology we propose to develop is general-purpose and can be readily adopted to new machine learning techniques, new datasets, and new research topics. It provides a novel method to integrate NASA observations, simulation results, and reanalysis data.” To learn more about the team's goals, click here.

For more information on NASA’s ESDT efforts, led by AIST (Advanced Information Systems Technology), click here.