SBIR Phase I: Automatic Generation of Physics-Informed AI Models

SBIR 第一阶段：自动生成基于物理的 AI 模型

• 批准号: 2037517
• 负责人: Hector Klie
• 金额: $ 25.56万
• 依托单位: DEEPCAST, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037517&HistoricalAwards=false
• 关键词: SBIR; Phase; Automatic; Generation; Physics

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to democratize the model building process for multiple industrial applications by: (1) making it easy to build models within hours instead of weeks, (2) cut model building costs by 10x or more, and (3) significantly mitigate risks by providing more accurate and interpretable models that are constrained by underlying physical principles. This technology would unlock a new generation of modeling workflows that are more scalable, have less uncertainty, and improve the structural visibility and interoperability of complex processes that are partially understood. As a result, companies would have tangible ways to reduce modeling costs, streamline optimization and control of operations and ultimately, achieve better decisions at economically viable rates. More broadly, the proposed technology should inspire various innovations and enhancements to products and services that strongly rely on physics modeling. It is expected that tangible results would induce profound effects across multiple sectors, including agriculture, environmental science, civil engineering, manufacturing, aerospace, construction, logistics, and medicine. This Small Business Innovation Research (SBIR) Phase I project will set the technical and business foundations required to automate and expedite the construction of reduced physics models from data with minimal human intervention. The goal for this project is to implement fast and reliable mechanisms for: (1) mapping spatio-temporal data of the physical world into structural network representations; (2) leverage the data from these network representations to generate a suitable set of equations that explain the underlying dynamics; (3) use advanced optimization techniques to find a list of models that capture the best combination of network and equations matching the observed data; and (4) orchestrate all these pieces into a single artificial intelligence platform that automatically consolidates data and human interactions for model building and visualization. Efforts in Phase I are particularly focused on demonstrating that the resulting models are physically sound and sufficiently robust for describing the dynamics of fluid transport and diffusion based on different amounts of available data and conditions.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）第一阶段项目的更广泛影响是通过以下方式使多个工业应用的模型构建过程民主化：（1）使在数小时而不是数周内轻松构建模型，（2）将模型构建成本削减10倍或更多，以及（3）通过提供受基本物理原理约束的更准确且可解释的模型来显着降低风险。这项技术将开启新一代的建模工作流程，这些工作流程更具可扩展性，不确定性更小，并提高了部分理解的复杂流程的结构可见性和互操作性。因此，公司将有切实的方法来降低建模成本，简化优化和控制操作，并最终以经济可行的价格实现更好的决策。更广泛地说，拟议的技术应该激发各种创新和增强产品和服务，强烈依赖物理建模。预计切实的成果将在农业、环境科学、土木工程、制造业、航空航天、建筑、物流和医学等多个部门产生深远的影响。该小型企业创新研究（SBIR）第一阶段项目将为自动化和加快从数据中构建简化物理模型所需的技术和业务基础，以最小的人为干预。该项目的目标是实现快速可靠的机制：（1）将物理世界的时空数据映射到结构网络表示;（2）利用这些网络表示的数据生成一组合适的方程，解释底层动力学;（3）使用高级优化技术来找到一系列模型，这些模型捕获与观测数据匹配的网络和方程的最佳组合;以及（4）将所有这些部分编排到一个人工智能平台中，该平台自动整合数据和人类交互，以进行模型构建和可视化。在第一阶段的努力特别集中在证明所产生的模型是物理上健全的，足够强大的描述流体传输和扩散的动力学的基础上不同数量的可用数据和conditions.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。