Elements: A Convergent Physics-based and Data-driven Computing Platform for Building Modeling

Elements：基于物理和数据驱动的融合计算平台，用于建筑建模

• 批准号: 2311685
• 负责人: Jianli Chen
• 金额: $ 58.41万
• 依托单位: University of Utah
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311685&HistoricalAwards=false
• 关键词: Elements; Convergent; Physics; based; Data

Building modeling is used to establish computational models of their physical characteristics, indoor environments and energy use with external weather conditions acting as inputs. An accurate building model supports a variety of downstream applications, such as smart building management, retrofit analysis, and decarbonization. Current building modeling practice uses either physics-based and data-driven approaches. Physics-based methods model building dynamics using physical principles, which are sound and reliable, yet often have to compromise accuracy due to high computational cost, limited mechanistic rules, and incomplete input information. Data-driven approaches are computationally efficient and offer flexibility, but they lack interpretability and often are difficult to extrapolate, which hinders their field applications. This project proposes to overcome these gaps by developing a novel cyberinfrastructure with an advanced integration mechanism to fulfill convergent physics-based and data-driven modeling. Research outcomes intend to enable accurate and computationally efficient building modeling in practice, and thereby benefit many relevant applications whose goals are a sustainable and resilient built environment. Education and outreach activities, including interdisciplinary curriculum development, minority and K12 student engagement, are closely integrated into specific research activities.This project will conduct unique, interdisciplinary research to design novel mechanisms that unify physics-based and data-driven modeling approaches. The project first plans to investigate and understand discrepancies between building models at different fidelities and actual measurements. Based upon developed understanding, several machine learning residual models and Neural Ordinary Differential Equations are developed to integrate physics-based and data-driven models. A flexible and easy-to-use cyberinfrastructure, including user interface layers and modeling layers, will be developed as an open-source platform to support convergent building modeling practice. The modeling framework and cyberinfrastructure are validated using field measurements from multiple resources. This project makes fundamental contributions to the building modeling field, as well as other engineering domains using diverse modeling approaches.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemical, Bioengineering, Environmental, and Transport Systems (ENG/CBET) and the Division of Civil, Mechanical & Manufacturing Innovation (ENG/CMMI).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.

建筑建模用于建立其物理特性、室内环境和能源使用的计算模型，外部天气条件作为输入。准确的建筑模型支持各种下游应用，如智能建筑管理、改造分析和脱碳。当前的建筑建模实践使用基于物理的方法和数据驱动的方法。基于物理的方法使用物理原理对建筑动力学进行建模，这些物理原理是合理和可靠的，但由于计算成本高，机械规则有限和输入信息不完整，因此通常不得不牺牲精度。数据驱动的方法计算效率高，提供灵活性，但它们缺乏可解释性，往往难以外推，这阻碍了他们的现场应用。该项目提出通过开发一种具有先进集成机制的新型网络基础设施来克服这些差距，以实现基于物理和数据驱动的融合建模。研究成果旨在使准确和计算效率的建筑建模在实践中，从而有利于许多相关的应用程序，其目标是一个可持续的和有弹性的建筑环境。教育和推广活动，包括跨学科课程开发，少数民族和K12学生参与，紧密结合到具体的研究活动中。该项目将进行独特的跨学科研究，以设计新的机制，统一基于物理和数据驱动的建模方法。该项目首先计划调查和了解不同高度的建筑模型与实际测量之间的差异。基于深入的理解，开发了几种机器学习残差模型和神经常微分方程，以集成基于物理和数据驱动的模型。一个灵活和易于使用的网络基础设施，包括用户界面层和建模层，将被开发为一个开源平台，以支持融合的建筑建模实践。使用来自多个资源的现场测量来验证建模框架和网络基础设施。该项目为建筑建模领域以及其他使用不同建模方法的工程领域做出了重要贡献。该奖项由高级网络基础设施办公室颁发，由化学、生物工程、环境和运输系统部门（ENG/CBET）和土木、机械制造创新部门共同支持&。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。

项目成果

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels

• DOI: 10.48550/arxiv.2310.05387
• 发表时间: 2023-10
• 作者: Da Long;Wei W. Xing;Aditi S. Krishnapriyan;R. Kirby;Shandian Zhe;Michael W. Mahoney

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes

• DOI: 10.48550/arxiv.2311.04465
• 发表时间: 2023-11
• 期刊: ArXiv
• 作者: Shikai Fang;Madison Cooley;Da Long;Shibo Li;R. Kirby;Shandian Zhe

Multi-Resolution Active Learning of Fourier Neural Operators

• DOI: 10.48550/arxiv.2309.16971
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: Shibo Li;Xin Yu;Wei W. Xing;Mike Kirby;Akil Narayan;Shandian Zhe

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor

连续索引张量的函数贝叶斯塔克分解

• 发表时间: 2024
• 期刊: Proceedings of The International Conference on Learning Representations (ICLR
• 作者: Fang, Shikai;Yu, Xin;Wang, Zheng;Li, Shibo;Kirby, Robert M.;Zhe, Shandian
• 通讯作者: Zhe, Shandian