Physics-Based Deep Learning: Convergence of Earth Science and Artificial Intelligence
Project Description

Improving prediction of the subseasonal-to-seasonal extreme weather and climate is a high priority to provide early warning and informed preparedness. Given the impacts of global warming and extreme weather to our community, you are invited to join us on this journey. By taking a giant leap to make the convergence of earth science and artificial intelligence as soon as possible to further improve the predictive ability of earth systems forecasting and modelling of long-range spatial connections across multiple timescales. Spatio-temporal Deep Learning will be an essential tool for understanding complex earth system science problems. Physics-Based Deep Learning, coupling physical process models with the versatility of data-driven machine learning will lead to major breakthrough in extreme weather and climate prediction, ocean modeling, geophysics and seismology.

Supervisor
WANG Yu-Hsing
Quota
3
Course type
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

AI programming

Applicant's Learning Objectives

To learn Spatio-temporal Deep Learning and Physics-Based Deep Learning

Complexity of the project
Challenging