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

数值模拟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和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，涵盖从微观到更大尺度相互作用的一系列物理尺度。传统的方法往往侧重于特定的规模，无法准确预测风险。随着DesignSafe CyberInfrastructure等社区数据存储库的出现，有效利用这些大型数据集来开发新的数据驱动模型以解决多尺度问题的机会尚未得到利用。此外，评估自然灾害的风险涉及一个复杂的相互关联的分析网络，这导致难以追踪推动最后决定的各种不确定因素。跟踪建模工作流可以确保决策过程是知情和透明的，并可以帮助决策者定义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跟踪，并帮助研究人员找到并有效地利用社区存储库中的大型数据集来开发新的理论。Cognitasium是一种由人工智能（AI）驱动的网络基础设施，它通过自动提取危险分析工作流程、使用相关信息增强大型社区数据集以进行分析以及使AI模型能够从海量数据集中发现新理论来解决这些挑战。Cognitasium是一个开源框架，可以很容易地适应各种社区。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风险评估的研究可重复性和透明度。该项目将把静态数据存储库转变为一个活跃的用户和开发人员社区，共同开发新的理论。通过可持续的软件实践和开放科学战略，它将支持自然灾害工程以外的大型用户社区。该项目的软件和工具可推广到其他领域，这些领域需要大量数据，需要多尺度模型和减少不确定性（例如，物理学和健康科学）。该项目包括四个具体的教育目标：通过Code@TACC培养未来的科学家，旨在使高中生能够规划，指导和培训本科研究人员，通过全国黑人工程师协会和代表性不足的大学提供的研讨会和培训，促进代表性不足的少数民族的保留，并通过文件，网络研讨会和暑期学院进行传播。围绕建模过程的不确定性可以对自然灾害工程（NHE）中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。相互关联的复杂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，交互式工具的出现，如笔记本电脑，改变了数据分析和探索。然而，Quixyter的交互性进一步加剧了跟踪工作流程的能力。因此，迫切需要自动提取工作流程，以纳入数据驱动的方法来量化和减少不确定性，并改善决策过程。Cognitasium是一个新的机器学习驱动的CI框架，用于NHE中的数据驱动发现。Cognitasium将成为NSF资助的DesignSafe CyberInfrastructure的基本组成部分，为广泛的NHE研究人员社区提供好处。该项目将：（i）通过参数化的Queryter笔记本中的工作流跟踪，在敏捷环境中实现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结合，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来改善自然灾害分析和飓风，风暴潮和地震的缓解。数据驱动的CI框架将推广到其他领域，这些领域需要大量数据和多尺度模型，并减少不确定性。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。

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.