SCIPE: Chishiki.ai: A sustainable, diverse, and integrated CIP community for Artificial Intelligence in Civil and Environmental Engineering

SCIPE：Chishiki.ai：土木与环境工程人工智能的可持续、多元化和综合 CIP 社区

• 批准号: 2321040
• 负责人: Krishna Kumar
• 金额: $ 699.93万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321040&HistoricalAwards=false
• 关键词: SCIPE; Chishiki.ai; sustainable; diverse; integrated; SCIPE; Chishiki.ai; sustainable; diverse; integrated

Chishiki.ai is an integrated community of CI professionals (CIPs) across artificial intelligence (AI) and civil and environmental engineering (CEE) practices to bolster U.S. infrastructure, aligning with thrust areas identified by the 2020 National Artificial Intelligence Initiative Act and the 2022 Infrastructure Investment and Jobs Act. THe Chishiki project adopts four strategies to build a sustainable, diverse, and integrated community of CI professionals for AI in CEE by (1) fostering collaboration between CIPs and domain experts through initiatives such as research summits, graduate and undergraduate fellowships, joint research initiatives, and industrial partnerships; (2) offering personalized and scalable learning environments powered by AI; (3) developing innovative AI-enabled CI architectures for reproducible and efficient workflows; and (4) creating a diverse, sustainable CIP community through engagement with historically underrepresented institutions through recruitment and research initiatives. Chishiki offers peer mentoring support and works with the NSF ACCESS Computational Science Support Network (CSSN) to support CI professionals in research activities related to CI and CEE. The integration of CIPs into CEE research is enabled through an active community of practice, providing opportunities for professional development, collaboration, and well-being. The project will publish best practices on partnerships, broadening adoption, and democratizing access to CI solutions in CEE. Chishiki offers AI-enhanced CI solutions and supports an integrated and diverse CIP community dedicated to transforming Civil and Environmental Engineering.Through Chishiki.ai, the project develops new courses for CI professionals to build and support sophisticated CI frameworks that foster AI-driven research innovations. The courses on AI4CI and CI4AI cover AI-enabled programming, AI-enhanced performance tuning of High-Performance Computing (HPC) systems, AI-driven knowledge discovery and curation, and building large-scale production-ready AI systems. The course on Scientific Machine Learning explores techniques for explainable AI, differentiable programming, and uncertainty propagation, thus enabling CI professionals to understand the need and use of AI in CEE. The Chishiki project develops a novel, scalable learning environment by building context-aware Large Language Models through reinforcement learning to generate personalized quizzes and explanations. The personalized AI tutor facilitates generating individualized quizzes and customized explanations to suit the individual's needs and learning abilities. The scalable and personalized AI tutor-powered courses will be available as open-access content on the Cornell Virtual Workshop (CVW) learning platform, reaching a broad community of CIPs. To accelerate AI-enhanced research, the project supports the development of sophisticated AI surrogates based on graph neural networks and differentiable simulations for optimization and engineering design, develops frameworks to deploy foundational AI models on memory-limited edge devices for structural health monitoring and transportation planning, and HPC systems to develop exemplar applications of AI-enabled CEE. The Chishiki project also supports AI-assisted code development to accelerate scientific research. The project's deliverables will be available on existing NSF-funded platforms, DesignSafe and the Texas Advanced Computing Center (TACC), broadening the adoption and integration of AI-enhanced CI innovations. The developments will be publicly accessible as open-course content and open-source solutions for broader dissemination. The project goal is to benefit more than 500 CIPs nationwide and to train more than 300,000 users worldwide through this personalized and scalable learning platform.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.

Chishiki.ai是一个整个人工智能（AI）（AI）和民用与环境工程（CEE）实践的CI专业人士（CIPS）的综合社区，以加强美国基础设施，与2020年《国家人工智能倡议法》和《 2022年基础设施投资和工作法案》一致。 Chishiki项目采用了四种策略，通过（1）通过研究峰会，研究峰会，研究生和本科研究计划，联合研究计划和工业合作伙伴来建立CI和域专家之间的CI专业人员为AI建立可持续，多样化和综合的CI专业人员社区； （2）提供由AI提供支持的个性化和可扩展的学习环境； （3）开发创新的AI支持AI的CI架构，用于可重复有效的工作流程； （4）通过招募和研究计划与历史上代表性不足的机构参与，建立一个多样化，可持续的CIP社区。 Chishiki提供同行指导支持，并与NSF访问计算科学支持网络（CSSN）合作，以支持CI专业人员在与CI和CEE相关的研究活动中。通过积极的实践社区，将CIPS整合到CEE研究中，为专业发展，协作和福祉提供了机会。该项目将发布有关合作伙伴关系，扩大采用率并在CEE中对CI解决方案的访问民主化的最佳实践。 Chishiki提供了AI增强的CI解决方案，并支持一个集成而多样的CIP社区，致力于改变民用和环境工程。通过Chishiki.ai，该项目为CI专业人员开发了新的课程，以促进CI专业人员建立和支持成熟的CI框架，从而促进AI-AI-DEREAD研究创新。 AI4CI和CI4AI上的课程涵盖了AI-AI-A-E-E-Enhanced性能调整，对高性能计算系统（HPC）系统，AI驱动的知识发现和策展以及构建大规模生产的AI Systems。科学机器学习的课程探索了可解释的AI，可区分编程和不确定性传播的技术，从而使CI专业人员能够了解CEE中AI的需求和使用。 Chishiki项目通过强化学习来产生个性化的测验和解释，从而开发了一个新颖，可扩展的学习环境，从而构建了上下文的大型语言模型。个性化的AI导师有助于产生个性化的测验和定制的解释，以适应个人的需求和学习能力。可扩展和个性化的AI辅导课程将作为康奈尔虚拟研讨会（CVW）学习平台上的开放访问内容提供，并到达广泛的CIPS社区。为了加速AI增强研究，该项目支持基于图形神经网络的复杂AI替代物的开发，以及可区分的模拟，以优化和工程设计，开发了框架，以在内存限制的边缘设备上部署基础AI模型，以用于结构性健康监测和运输计划，以及HPC系统，以及HPC系统，以开发具有AI-II型CEEE的样本应用程序。 Chishiki项目还支持AI辅助代码开发，以加速科学研究。该项目的可交付成果将在现有的NSF资助平台，DesignSafe和Texas Advanced Computing Center（TACC）上提供，扩大了AI增强CI创新的采用和集成。这些发展将作为开放式内容和开源解决方案公开访问，以进行更广泛的传播。项目的目标是通过这个个性化和可扩展的学习平台在全国范围内受益500多个CIPS，并通过该奖项反映了NSF的法定任务，并通过基金会的智力优点和更广泛的影响审查标准来培训全球300,000多名用户。