Collaborative Research: OAC Core: Learning AI Surrogate of Large-Scale Spatiotemporal Simulations for Coastal Circulation

Nearly 900 million people live at the front line of the climate crisis in low-lying coastal zones and are 15 times more likely to die from flooding and storms. Scientists need to simulate ocean current circulation along the coasts to develop early warning systems that could save countless lives and prevent significant annual losses in developing countries that are most vulnerable to the impacts of climate change.

Traditionally, such simulations are conducted by running numerical models on a high-performance computing (HPC) platform, which is both expensive and time-consuming. The last few years have witnessed a rapid transformation of the field driven by advances in deep learning and the emerging Graphics Processing Unit (GPU) computational architecture. The main idea is to train neural network surrogates of numerical models, and once pre-trained, the networks can generate simulations with much faster speed and a smaller energy footprint.

The project will develop a novel AI surrogate cyberinfrastructure for large-scale spatiotemporal simulations in coastal circulation. Educational activities will include curriculum development, mentoring a broad group of high school students in AI seminars at Summer Camps, as well as year-long projects for a selected number of high school students for the regional Science Fair competition.

The project will provide several innovations in AI and cyberinfrastructure research. First, it will investigate a novel AI surrogate model architecture to capture the unique spatiotemporal data characteristics of coastal circulation simulations. Second, it will explore several strategies to optimize the model for time and GPU memory efficiency. Finally, it will design and implement a scalable model training and inference pipeline on a multi-GPU cluster.

This project is funded by the National Science Foundation's National Discovery Cloud for Climate initiative.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.