CAREER: Accelerating Probabilistic Predictions of Sea-level Rise with Deep Learning

职业：利用深度学习加速海平面上升的概率预测

• 批准号: 2238316
• 负责人: Douglas Brinkerhoff
• 金额: $ 62.64万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238316&HistoricalAwards=false
• 关键词: CAREER; Accelerating; Probabilistic; Predictions; Sea

A new paradigm has recently emerged in predicting changes to Earth’s glaciers that emphasizes accounting for unknowns with respect to ice physics and future climate in order to construct a range of plausible futures. Such probability-based bounds are essential in preparing for sea level rise, ecological changes, and other climate feedbacks in which understanding both best- and worst-case scenarios is of practical importance. This task is computationally difficult: glacier models are expensive, particularly when it can take months or years to run the many thousands of simulations required to completely characterize possible outcomes. However, understanding this distribution is essential in meeting the grand challenge of building a sustainable future. This project will use deep learning to construct surrogate models, replacements for ice sheet model components that approximate the original model, but which are much less computationally costly. In addition, this project will convene a summer school and develop educational models to integrate the research and educational components of the work. In particular, this project will use geometric and generative deep learning in tandem with novel applications of classic techniques in high-performance computing to build an approximate (or surrogate) model that is hundreds of times faster than traditional ice sheet models. Such a speedup will in turn allow researchers to explore an unprecedented range of future scenarios and meet the challenges of ice modeling for understanding future global impacts. In addition, a nine-day summer school will bring together instructors and students from around the globe to learn and share effective methods for applying deep learning to problems in glaciology. To help high schoolers learn the skills needed to understand climate change and its uncertainties and to use computers to tackle the associated scientific, economic, and policy challenges, we will develop educational modules tailored towards high school students exploring the intersection of programming, data, and global change.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

最近出现了一个新的范式，以预测地球冰川的变化，该变化强调了关于冰物理学和未来气候的未知数，以构建一系列合理的未来。这种基于概率的界限对于准备海平面上升，生态变化和其他气候反馈至关重要，在这种情况下，理解最佳和最坏情况的情况至关重要。该任务在计算上很困难：冰川模型很昂贵，尤其是当可能需要数月或数年的时间运行数千个模拟以完全表征可能的结果所需的模拟时。但是，了解这种分布对于应对建立可持续未来的巨大挑战至关重要。该项目将使用深度学习来构建替代模型，替换近似原始模型但计算成本少得多的冰盖模型组件。此外，该项目将召集一所暑期学校，并开发教育模式，以整合工作的研究和教育组成部分。特别是，该项目将与经典技术在高性能计算中的新颖应用中使用几何和通用的深度学习，以构建比传统冰盖模型快数百倍的近似（或替代）模型。这样的加速反过来，研究人员可以探索前所未有的未来场景，并应对了解未来全球影响的冰建模的挑战。此外，为期9天的暑期学校将召集来自全球的讲师和学生，以学习和分享将深度学习应用于冰川学问题的有效方法。为了帮助高中生学习了解气候变化及其不确定性所需的技能，并使用计算机应对相关的科学，经济和政策挑战，我们将发展教育。针对高中生量身定制的模块，探索节目，数据和全球变化的交集。该项目由地球科学局共同资助了地理学家的AI/ML进步。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的criperia criperia criperia criperia criperia crightia criptia the Issportiation the奖励。