SBIR Phase II: Physics-Informed Machine Learning Emulators to Model Physical Spatio-Temporal Processes for Climate and Weather Risk Forecasting

SBIR 第二阶段：基于物理的机器学习模拟器，用于模拟气候和天气风险预测的物理时空过程

• 批准号: 1951266
• 负责人: Hunter Connell
• 金额: $ 75万
• 依托单位: TERRAFUSE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951266&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Physics; Informed

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to provide commercially-deployable technology for highly-scalable, spatially-granular, and cost-effective risk predictions of climate-driven events, such as wildfire spread, from real-time to yearly time scales. As insured losses due to wildfires have increased over fivefold in the last decade, the associated risk makes it critical to improve the ability to predict physical and financial impacts at scale. Current predictive technologies used in major industries, like energy and insurance, are based on complex, hand-engineered, and computationally-intensive numerical physics models of climate and weather. In contrast, the proposed technology develops special AI emulator systems that learn the relevant physics and key drivers, including wind and surface hydrology, in wildfires. The proposed system can perform predictions much more efficiently due to a far simpler computational workflow and native AI hardware acceleration. In addition, AI emulators automate the assimilation of vastly higher amounts of remote-sensing and other observational data (e.g., radar measurements from weather satellites or land cover and vegetation data) over numerical models, allowing for increased accuracy, continuous improvement, and dynamic predictions reflecting changing on-the-ground conditions. This Small Business Innovation Research (SBIR) Phase II project addresses the pressing need in the energy and insurance industries to accurately and consistently assess wildfire risk over large geographical regions and at a localized level, on time scales ranging from daily to yearly. The proposed R&D will focus on developing and validating an AI emulator of wildfire spread. This entails 1) developing AI architectures for assimilating observational (remote-sensing) and numerical simulation data on drivers of wildfire at different temporal and spatial scales, including vegetation, soil hydrology, and atmospheric winds; 2) integrating data on historical wildfires and their spread to drive the learning process; 3) conducting extensive verification and validation studies; and 4) developing and deploying APIs and graphical interfaces for accessing AI emulator output on the cloud.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.

这个小企业创新研究（SBIR）第二阶段项目的更广泛影响是提供商业部署的技术，用于对野火蔓延等气候驱动事件进行高度可扩展、空间粒度和成本效益高的风险预测，从实时到年度时间尺度。由于野火造成的保险损失在过去十年中增加了五倍以上，因此相关风险使得提高大规模预测物理和财务影响的能力至关重要。 目前用于能源和保险等主要行业的预测技术是基于复杂的、手工设计的、计算密集型的气候和天气数值物理模型。 相比之下，拟议的技术开发了特殊的人工智能仿真器系统，可以学习野火中的相关物理和关键驱动因素，包括风和地表水文。 由于更简单的计算工作流程和原生AI硬件加速，所提出的系统可以更有效地执行预测。此外，人工智能模拟器还可以自动同化大量的遥感和其他观测数据（例如，气象卫星的雷达测量数据或土地覆盖和植被数据）优于数值模型，从而可以提高准确性，持续改进和动态预测，反映不断变化的地面条件。这个小型企业创新研究（SBIR）第二阶段项目解决了能源和保险行业的迫切需求，以准确和一致地评估大地理区域和本地化水平的野火风险，时间范围从每天到每年。拟议的研发将重点开发和验证野火蔓延的人工智能模拟器。 这需要1）开发AI架构，以吸收观察（2）整合关于历史野火及其蔓延的数据，以推动学习过程;（3）进行广泛的验证和确认研究;该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。