CRII: OAC: A Multi-fidelity Computational Framework for Discovering Governing Equations Under Uncertainty

CRII：OAC：用于发现不确定性下控制方程的多保真度计算框架

• 批准号: 2348495
• 负责人: Subhayan De
• 金额: $ 17.37万
• 依托单位: Northern Arizona University
• 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: 2024-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348495&HistoricalAwards=false
• 关键词: CRII; OAC; Multi; fidelity; Computational

Understanding the nature of physical systems has always been an essential curiosity of humankind, enabling the discovery of many fundamental physical laws governing these systems and processes. Often, in the process, it was seen that a more straightforward explanation is preferred -- embodied in the famous Occam's razor or principle of parsimony. With a similar goal, this project conducts fundamental research seeking to advance the state-of-the-art in understanding the parsimonious principles of physics governing the behavior of a complex physical system under uncertainty. The resulting software framework enables the users to utilize data from a wide range of physical systems to unlock the important aspects of their behavior by analyzing the identified mathematical equations. With this framework, the project investigator provides the community of researchers and educators with a powerful and amenable tool to comprehend and predict the actual behavior of various real-world systems in the presence of uncertainty. Harnessing already developed knowledge in understanding simplified physical systems with similar behavior, the framework builds a novel paradigm using interpretable deep learning for discovering parsimonious governing equations to describe the behavior of complex physical systems under uncertainty. New approaches to utilizing low-fidelity models in defining the system's behavior are also explored, providing even more computationally efficient tools to incorporate prior knowledge. These advancements and the developed software allow the framework to be applied to various application domains. The framework is evaluated with testbed problems such as the behavior of a 20-story building and a benchmark turbulence modeling problem. Furthermore, the resulting framework is applied to other critical problems of complex nature through collaborations, e.g., the spread of wildland fires, enzyme-catalyzed reactions, and the spread of pollutant plumes. The project also emphasizes the importance of identifying parsimonious governing equations to describe the behavior of complex systems through education and outreach activities. Results from the project are submitted to reputable journals and presented at national and international conferences. One graduate student is mentored as part of the project, and findings are incorporated into an undergraduate probability and machine learning course. K-12 outreach efforts led by the project investigator include modules on the discovery of equations, machine learning basics, and using software to infer from data based on the research outputs from this project presented at middle/high school summer workshop programs.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.

理解物理系统的本质一直是人类的基本好奇心，使许多基本的物理定律发现这些系统和过程。通常，在这个过程中，人们看到一个更直接的解释是首选-体现在著名的奥卡姆剃刀或简约原则。出于类似的目标，该项目进行基础研究，寻求推进最先进的理解物理学的简约原则，管理不确定性下复杂物理系统的行为。由此产生的软件框架使用户能够利用来自广泛的物理系统的数据，通过分析识别的数学方程来解锁其行为的重要方面。有了这个框架，项目研究人员为研究人员和教育工作者提供了一个强大而可靠的工具，以理解和预测各种现实世界系统在不确定性存在下的实际行为。利用已经开发的知识来理解具有类似行为的简化物理系统，该框架使用可解释的深度学习来建立一个新的范式，以发现简约的控制方程来描述不确定性下复杂物理系统的行为。还探讨了利用低保真度模型定义系统行为的新方法，提供了更有效的计算工具来结合先验知识。这些进步和开发的软件允许该框架应用于各种应用领域。该框架进行了评估，如一个20层的建筑物和基准湍流建模问题的行为测试台的问题。此外，所产生的框架通过合作应用于其他复杂性质的关键问题，例如，野火的蔓延、酶催化反应以及污染物羽流的蔓延。该项目还强调了通过教育和推广活动确定简约控制方程来描述复杂系统行为的重要性。该项目的成果已提交给知名期刊，并在国家和国际会议上发表。作为该项目的一部分，一名研究生受到指导，研究结果被纳入本科生概率和机器学习课程。由项目研究员领导的K-12外展工作包括方程发现、机器学习基础知识以及使用软件根据该项目在初中/高中夏季研讨会项目上提出的研究成果进行数据推断等模块。该奖项反映了NSF的法定使命，并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，被认为值得支持。