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

合作研究：OAC Core：学习沿海环流大规模时空模拟的人工智能替代品

• 批准号: 2402947
• 负责人: Jie Ren
• 金额: $ 20万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402947&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Learning

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.

在低洼沿海地区，近9亿人生活在气候危机的前线，死于洪水和暴风雨的可能性高15倍。科学家需要模拟海岸沿海的海洋流通，以开发预警系统，这些警告系统可以挽救无数生命，并防止发展中国家最容易受到气候变化影响的发展中国家的巨大损失。传统上，这样的模拟是通过在高性能计算（HPC）平台上运行数值模型来进行的，该平台既昂贵又耗时。在过去的几年中，由于深度学习和新兴图形处理单元（GPU）计算体系结构的进步，该领域的迅速转变。主要思想是训练数值模型的神经网络替代物，一旦预先训练，这些网络就可以以更快的速度和较小的能量足迹生成模拟。该项目将开发一种新型的AI替代网络基础设施，用于沿海循环中的大规模时空模拟。教育活动将包括课程开发，在夏令营的AI研讨会中指导大量的高中生，以及为地区科学公平竞赛选定的高中学生提供的为期一年的项目。该项目将在AI和网络基础设施研究中提供一些创新。首先，它将研究一种新型的AI替代模型结构，以捕获沿海循环模拟的独特时空数据特征。其次，它将探索几种策略，以优化时间和GPU内存效率。最后，它将在多GPU群集上设计和实施可扩展的模型培训和推理管道。该项目由国家科学基金会的国家气候云倡议云资助。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。