Elements: Cognitasium - Enabling Data-Driven Discoveries in Natural Hazards Engineering

Elements：Cognitasium - 实现自然灾害工程中数据驱动的发现

• 批准号: 2103937
• 负责人: Krishna Kumar
• 金额: $ 55.63万
• 依托单位: University of Texas at Austin
• 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: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103937&HistoricalAwards=false
• 关键词: Elements; Cognitasium; Enabling; Data; Driven

Numerical modeling plays a critical role in assessing and mitigating risks posed by natural hazards, such as the risks to coastal communities from hurricanes and the threats to infrastructure from earthquakes. Accurately predicting these hazards requires modeling the multi-scale nature of these problems, covering a range of physical scales from microscopic to kilometer-scale interactions. Traditional approaches often focus on a particular scale and are incapable of predicting the risks accurately. With the advent of community data repositories such as the DesignSafe CyberInfrastructure, there is an as-yet untapped opportunity to effectively use these large datasets to develop new data-driven models to solve multi-scale problems. Furthermore, assessing the risks of natural hazards involves a complex web of interconnected analyses, which leads to difficulties in tracking the various uncertainties driving the final decision. Tracking the modeling workflow can ensure decision processes are informed and transparent and can help decision-makers define their confidence in model results. To support these needs, new methods are required to automate the workflow tracking and help researchers find and effectively utilize the large datasets in community repositories to develop new theories. Cognitasium, an Artificial Intelligence (AI)-powered cyberinfrastructure, addresses these challenges by automatically extracting the hazard analysis workflows, augmenting large community datasets with relevant information for analysis, and enabling AI models to discover new theories from massive datasets. Cognitasium is being developed as an open-source framework and can be easily adapted to a variety of communities. The project uncovers the possibility of discovering new theories by combining field and experimental data with numerical simulations in large community datasets. The automated tracking of workflows improves the research reproducibility and transparency in risk assessment. The project will transform static data repositories into an active community of users and developers working together to develop new theories. With sustainable software practices and open science strategy, it will support a large community of users beyond natural hazard engineering. The software and tools from the project are generalizable to other fields with massive data requirements and the need for multi-scale models and reduced uncertainties (e.g., physics and health sciences). The project incorporates four specific educational objectives: Inspire future scientists through Code@TACC aimed at enabling high-school students to program, mentor and train undergraduate researchers, facilitate the retention of underrepresented minorities through workshops and training offered at the National Society of Black Engineers and underrepresented colleges, and dissemination through documentation, webinars, and summer institutes. Uncertainties surrounding the modeling process can have important implications for the decision-making process in Natural Hazard Engineering (NHE). Assessing the risks of natural hazards is a complex process involving numerical simulations, integrated field and lab characterization, and uncertainty quantification. The complex web of inter-connected analysis leads to an inability to track workflows and accurately propagate the associated uncertainties, thus impacting the decision-making process. Meanwhile, the emergence of interactive tools such as Jupyter Notebooks has transformed data analysis and exploration. However, the interactive nature of Jupyter has further exasperated the ability to track workflows. Hence, there is an urgent need for automatically extracting workflows to incorporate a data-driven approach to quantify and reduce uncertainty and improve the decision-making process. Cognitasium is a novel machine-learning-powered CI framework for data-driven discoveries in NHE. Cognitasium will become a fundamental component of the NSF-funded DesignSafe CyberInfrastructure offering benefits to a broad community of NHE researchers. The project will: (i) enable end-to-end integration of uncertainty propagation in agile environments through workflow tracking in parameterized Jupyter notebooks, (ii) build knowledge graphs that integrate experimental and field data with numerical analysis to develop new multi-scale models, and (iii) support scalable machine learning to solve complex multi-scale problems with large datasets. The AI framework will improve natural hazard analysis and mitigation of hurricanes, storm surge, and earthquakes through data-driven discoveries. The data-driven CI framework will be generalizable to other fields with massive data and the need for multi-scale models and reduced uncertainties.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.

数值模型在评估和减轻自然灾害造成的风险方面发挥着关键作用，例如飓风对沿海社区的风险以及地震对基础设施的威胁。准确预测这些危害需要对这些问题的多尺度性质进行建模，涵盖从微观到公里尺度相互作用的一系列物理尺度。传统方法通常关注特定规模，无法准确预测风险。随着 DesignSafe Cyber​​Infrastructure 等社区数据存储库的出现，有一个尚未开发的机会来有效地使用这些大型数据集来开发新的数据驱动模型来解决多尺度问题。此外，评估自然灾害风险涉及复杂的相互关联的分析网络，这导致难以跟踪推动最终决策的各种不确定性。跟踪建模工作流程可以确保决策过程知情且透明，并可以帮助决策者确定他们对模型结果的信心。为了支持这些需求，需要新的方法来自动化工作流程跟踪，并帮助研究人员找到并有效利用社区存储库中的大型数据集来开发新理论。 Cognitasium 是一种由人工智能 (AI) 驱动的网络基础设施，它通过自动提取危害分析工作流程、用相关信息扩充大型社区数据集进行分析，以及使 AI 模型能够从海量数据集中发现新理论来应对这些挑战。 Cognitasium 正在开发为开源框架，可以轻松适应各种社区。该项目揭示了通过将现场和实验数据与大型社区数据集中的数值模拟相结合来发现新理论的可能性。工作流程的自动跟踪提高了研究的可重复性和风险评估的透明度。该项目将把静态数据存储库转变为用户和开发人员共同开发新理论的活跃社区。凭借可持续的软件实践和开放科学策略，它将支持自然灾害工程之外的大量用户社区。该项目的软件和工具可推广到具有大量数据需求、需要多尺度模型和减少不确定性的其他领域（例如物理和健康科学）。该项目包含四个具体的教育目标：通过 Code@TACC 激励未来的科学家，旨在使高中生能够编程、指导和培训本科研究人员；通过国家黑人工程师协会提供的研讨会和培训促进保留代表性不足的少数族裔；以及代表性不足的大学，并通过文档、网络研讨会和暑期学院进行传播。建模过程的不确定性可能会对自然灾害工程 (NHE) 的决策过程产生重要影响。评估自然灾害风险是一个复杂的过程，涉及数值模拟、综合现场和实验室表征以及不确定性量化。复杂的互连分析网络导致无法跟踪工作流程并准确传播相关的不确定性，从而影响决策过程。与此同时，Jupyter Notebooks 等交互工具的出现改变了数据分析和探索。然而，Jupyter 的交互性质进一步削弱了跟踪工作流程的能力。因此，迫切需要自动提取工作流程以结合数据驱动的方法来量化和减少不确定性并改进决策过程。 Cognitasium 是一种新颖的机器学习驱动的 CI 框架，用于 NHE 中数据驱动的发现。 Cognitasium 将成为 NSF 资助的 DesignSafe Cyber​​Infrastruct 的基本组成部分，为广泛的 NHE 研究人员社区带来好处。该项目将：(i) 通过参数化 Jupyter 笔记本中的工作流程跟踪，实现敏捷环境中不确定性传播的端到端集成，(ii) 构建知识图，将实验和现场数据与数值分析相集成，以开发新的多尺度模型，(iii) 支持可扩展的机器学习，以解决大型数据集的复杂多尺度问题。人工智能框架将通过数据驱动的发现改进自然灾害分析以及飓风、风暴潮和地震的缓解。数据驱动的 CI 框架将推广到具有海量数据、需要多尺度模型和减少不确定性的其他领域。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的影响进行评估，认为值得支持审查标准。