CAREER: Domain-aware Statistical Learning

职业：领域感知统计学习

• 批准号: 2143695
• 负责人: Xiao Liu
• 金额: $ 50.02万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143695&HistoricalAwards=false
• 关键词: CAREER; Domain; aware; Statistical; Learning

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national competitiveness by transforming how governing physics and engineering domain knowledge is integrated into data-driven models for high-stakes applications. Applications in domain-knowledge intensive engineering environments such as energy infrastructure, aviation safety, and manufacturing require interpretable models, explainable decisions and actionable insights. In these environments, the old paradiam of “letting the data speak for themselves” is being replaced by the capability of “letting the data speak based on the laws of physics and engineering”. This project will address the development of methods to integrate data with physics-based models in three main use cases, namely environmental processes to enhance resilience of our national utilities during extreme events intensified by climate change; thermal modeling to improve energy efficiency in Data Center operations; and structural dynamics to enhance aviation safety in an increasingly crowded airspace. The accompanying educational plan aims to address the gaps between general-purpose data science education at the school and university level and the specific needs for next-generation engineering students with diverse backgrounds. The educational plan also aims to improve data literacy among the general public by improving awareness of the increasing availability of data and the capability of interpreting those data through local community activities.This research establishes a new Structure-Exploiting-Preserving (SEP) domain-aware statistical learning paradigm that enables the direct embedding of governing physics into data-driven models during model construction. Unlike existing approaches that impose governing physics as auxiliary regularizations or constraints, the SEP framework will enable the embedding of data-driven models into the solution space of governing physics (i.e., governing physics will no longer be used as auxiliary regularizations, but an inherent component that is directly integrated into data-driven models during model construction). This is achieved by exposing the solution structure of governing physics (i.e., structure exploiting), and embedding data-driven models into the solution space of governing equations (i.e., structure preserving). Under the SEP framework and through the collaboration with industry partners, the research will initiate a trajectory that leads to a set of new methodologies for domain-aware statistical modeling, data-driven discovery of governing physics, and dynamic sampling for non-stationary engineering processes.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.

这项学院早期职业发展计划(Career)赠款将通过改变管理物理和工程领域知识的方式，将其整合到高风险应用程序的数据驱动模型中，从而有助于提高国家竞争力。在能源基础设施、航空安全和制造等领域知识密集型工程环境中的应用需要可解释的模型、可解释的决策和可操作的见解。在这些环境中，“让数据自己说话”的旧模式正在被“让数据根据物理和工程定律说话”的能力所取代。该项目将在三个主要使用案例中开发将数据与基于物理的模型相结合的方法，即环境进程，以增强我国国家公用事业在气候变化加剧的极端事件中的应变能力；热模拟，以提高数据中心业务的能效；以及结构动力学，以加强日益拥挤的空域的航空安全。随附的教育计划旨在解决中小学和大学一级的通用数据科学教育与具有不同背景的下一代工程专业学生的具体需求之间的差距。该教育计划还旨在通过提高公众对数据可用性的认识以及通过当地社区活动解释这些数据的能力来提高公众的数据素养。本研究建立了一种新的结构-利用-保存(SEP)领域感知的统计学习范式，能够在模型构建过程中将管理物理直接嵌入到数据驱动的模型中。与将支配物理强加为辅助正则化或约束的现有方法不同，SEP框架将使数据驱动模型能够嵌入到支配物理的解空间中(即，支配物理将不再用作辅助正则化，而是在模型构建期间直接集成到数据驱动模型中的固有组件)。这是通过揭示控制物理的解结构(即结构利用)，并将数据驱动模型嵌入控制方程的解空间(即结构保持)来实现的。在SEP框架下，通过与行业合作伙伴的合作，这项研究将启动一套新的方法，用于领域感知统计建模、控制物理的数据驱动发现和非平稳工程过程的动态采样。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions

非线性动力系统的统计学习及其在飞机-无人机碰撞中的应用

• DOI: 10.1080/00401706.2023.2203175
• 发表时间: 2023
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Liu, Xinchao;Liu, Xiao;Kaman, Tulin;Lu, Xiaohua;Lin, Guang
• 通讯作者: Lin, Guang

Regression Trees on Grassmann Manifold for Adapting Reduced-Order Models

用于适应降阶模型的格拉斯曼流形回归树

• DOI: 10.2514/1.j062180
• 发表时间: 2023
• 期刊: AIAA Journal
• 影响因子: 2.5
• 作者: Liu, Xiao;Liu, Xinchao
• 通讯作者: Liu, Xinchao

Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes

估算时空平流扩散过程初始条件的反演模型

• DOI: 10.1080/00401706.2023.2181222
• 发表时间: 2023
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Liu, Xiao;Yeo, Kyongmin
• 通讯作者: Yeo, Kyongmin