CAREER: HayaRupu: Accelerating Natural Hazard Engineering with AI-Driven Discovery Loops

职业：HayaRupu：利用人工智能驱动的发现循环加速自然灾害工程

• 批准号: 2339678
• 负责人: Krishna Kumar
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339678&HistoricalAwards=false
• 关键词: CAREER; HayaRupu; Accelerating; Natural; Hazard

The HayaRupu project aims to accelerate advancements in engineering and scientific research through artificial intelligence (AI), utilizing landslide hazards as a key area for demonstration. Central to this initiative is the AI-accelerated scientific discovery loop, a framework that integrates AI into every stage of scientific inquiry, from knowledge discovery to hypothesis testing and modeling. This approach leverages machine learning (ML) for sophisticated pattern recognition and anomaly detection, enabling the extraction of new insights from complex datasets. A significant focus of HayaRupu is identifying and addressing knowledge gaps in natural hazard engineering by building a context-aware knowledge graph. Furthermore, the project innovates by integrating AI in numerical simulations, exploiting the speed of AI and the accuracy of numerical simulations to develop novel optimization strategies. The physics-aware AI methods bridge the gap between simulated environments and real-world applications. HayaRupu's approach exemplifies how physics-aware AI can accelerate scientific progress. A key aspect of HayaRupu is its educational outreach, which involves creating new AI-assisted scalable and personalized learning for engineering students. This educational initiative supports the development of future engineers with skills in cutting-edge AI technologies, enhancing diversity in STEM fields and contributing to a skilled workforce adept in integrating AI and natural hazard engineering.The HayaRupu framework (Japanese for FastLoop) will accelerate discoveries in natural hazards engineering (NHE) by applying Artificial Intelligence (AI) to facilitate knowledge discovery and accelerate NextGen AI-embedded simulation tools for exascale simulations through physics-aware AI techniques. The work provides novel AI solutions to accelerate discoveries in natural hazard engineering. It has three key intellectual merits: (i) context-aware knowledge graphs as reasoning engines to enable new data-driven discoveries and derive fundamental multi-scale equations through geometric deep learning, (ii) building the NextGen AI-accelerated differentiable simulators offering a new paradigm for solving inverse and design problems, (iii) creating an integrated framework of Large Language Model-enabled robust end-to-end automated workflow design to demonstrate the potential of AI in driving scientific advances. HayaRupu is developing a personalized and scalable AI tutor to transform engineering education by offering future engineers a personalized learning environment, quizzes, and support. Key educational outreach initiatives include organizing Tween Code Clubs at the Austin Public Library to promote computational and AI literacy among young learners and offering targeted programs in low-income and underrepresented communities. Additionally, the project collaborates with the Code@TACC program to inspire high school students, especially those from marginalized backgrounds, towards STEM careers. Through these efforts, HayaRupu advances scientific understanding and fosters a diverse and inclusive environment in science and technology education.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.

Hayarupu项目旨在通过人工智能（AI）加速工程和科学研究的进步，利用滑坡危害作为示范的关键领域。该计划的核心是AI-ACLEAR的科学发现循环，该框架将AI整合到科学探究阶段，从知识发现到假设测试和建模。这种方法利用机器学习（ML）来进行复杂的模式识别和异常检测，从而可以从复杂数据集中提取新的见解。 Hayarupu的一个重要重点是通过构建情境感知的知识图来识别和解决自然危害工程中的知识差距。此外，该项目通过将AI集成在数值模拟中，利用AI速度以及数值模拟的准确性来开发新型优化策略来创新。物理意识AI方法弥合了模拟环境和现实世界应用之间的差距。 Hayarupu的方法体现了物理学意识AI如何加速科学进步。 Hayarupu的一个关键方面是其教育外展，其中涉及为工程专业的学生创建新的AI辅助可扩展和个性化学习。 This educational initiative supports the development of future engineers with skills in cutting-edge AI technologies, enhancing diversity in STEM fields and contributing to a skilled workforce adept in integrating AI and natural hazard engineering.The HayaRupu framework (Japanese for FastLoop) will accelerate discoveries in natural hazards engineering (NHE) by applying Artificial Intelligence (AI) to facilitate knowledge discovery and accelerate NextGen通过物理学AI技术，AI嵌入的模拟工具用于外部模拟。这项工作提供了新颖的AI解决方案，以加速自然危害工程中的发现。它具有三个关键的智力优点：（i）上下文感知的知识图作为推理引擎，以实现新的数据驱动的发现并通过几何深度学习来得出基本的多尺度方程，（ii）构建NextGen ai a-ai-ai-ai a-accelaperiage-celleceliable-celpeliable-celperiable-clase-clase-celperianceliancelable simulators，为求解型号的新范围，以求解了较大的竞争框架，（iii ii ii ii），（iii ii ii III），（iii iii），（iii iii）证明AI在推动科学进步方面的潜力。 Hayarupu通过为未来的工程师提供个性化的学习环境，测验和支持来开发一个个性化且可扩展的AI导师，以改变工程教育。主要的教育外展计划包括在奥斯汀公共图书馆组织补间代码俱乐部，以促进年轻学习者中的计算和AI识字率，并在低收入和代表性不足的社区中提供有针对性的计划。此外，该项目还与@TACC计划合作，以激发高中生，尤其是从边缘化背景的学生，朝着STEM职业发展。通过这些努力，Hayarupu提高了科学的理解，并促进了科学技术教育中多样化和包容性的环境。该奖项反映了NSF的法定使命，并且使用基金会的知识分子优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。