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
基本信息
- 批准号:2339678
- 负责人:
- 金额:$ 50万
- 依托单位:
- 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:
- 关键词:
项目摘要
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,将滑坡灾害作为关键的示范领域。这一计划的核心是人工智能加速的科学发现循环,这是一个将人工智能集成到科学探究的每个阶段的框架,从知识发现到假设检验和建模。这种方法利用机器学习(ML)进行复杂的模式识别和异常检测,从而能够从复杂的数据集中提取新的见解。HayaRupu的一个重要重点是通过构建一个上下文感知的知识图来识别和解决自然灾害工程中的知识差距。此外,该项目通过将人工智能集成到数值模拟中,利用人工智能的速度和数值模拟的准确性来开发新的优化策略。物理感知人工智能方法弥合了模拟环境和现实世界应用程序之间的差距。HayaRupu的方法证明了物理感知AI如何加速科学进步。HayaRupu的一个关键方面是其教育推广,其中包括为工程专业学生创建新的人工智能辅助的可扩展和个性化学习。这项教育计划支持未来工程师的发展,他们拥有尖端人工智能技术的技能,增强STEM领域的多样性,并为熟练的劳动力队伍做出贡献,这些劳动力擅长整合人工智能和自然灾害工程。(日语为FastLoop)将通过应用人工智能(AI)促进知识发现并加速下一代AI,加速自然灾害工程(NHE)的发现-嵌入式仿真工具,通过物理感知的AI技术进行exascale仿真。这项工作提供了新的人工智能解决方案,以加速自然灾害工程的发现。它有三个关键的知识价值:(i)上下文感知知识图作为推理引擎,以实现新的数据驱动发现,并通过几何深度学习导出基本的多尺度方程,(ii)构建下一代人工智能加速可微模拟器,为解决逆向和设计问题提供新的范式,(iii)创建一个支持大型语言模型的强大端到端自动化工作流程设计的集成框架,以展示人工智能在推动科学进步方面的潜力。HayaRupu正在开发一个个性化和可扩展的人工智能导师,通过为未来的工程师提供个性化的学习环境,测验和支持来改变工程教育。主要的教育推广计划包括在奥斯汀公共图书馆组织Tween Code Clubs,以促进年轻学习者的计算和人工智能素养,并在低收入和代表性不足的社区提供有针对性的计划。此外,该项目与Code@TACC计划合作,激励高中生,特别是那些来自边缘化背景的学生,走向STEM职业。通过这些努力,HayaRupu促进了科学理解,并在科学和技术教育中营造了一个多元化和包容性的环境。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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Krishna Kumar其他文献
Introductory operations research
运筹学入门
- DOI:
- 发表时间:
2004 - 期刊:
- 影响因子:0
- 作者:
H. S. Kasana;Krishna Kumar - 通讯作者:
Krishna Kumar
Klippel Trenaunay Syndrome: a rare case report in a neonate
Klippel Trenaunay 综合征:新生儿罕见病例报告
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Krishna Kumar;Nanjappa Mohan Kumar - 通讯作者:
Nanjappa Mohan Kumar
Undescended Testis: A Plea for Early Diagnosis and Optimal ManagementKumar G1*
未降睾丸:呼吁早期诊断和最佳管理Kumar G1*
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Krishna Kumar - 通讯作者:
Krishna Kumar
Ultrastructural features of the larval Malpighian tubules of the flesh fly Sarcophaga ruficornis (Diptera: Sarcophagidae)
肉蝇Sarcophaga ruficornis(双翅目:Sarcophagidae)幼虫马氏小管的超微结构特征
- DOI:
10.1017/s1742758412000252 - 发表时间:
2012 - 期刊:
- 影响因子:1.2
- 作者:
Ruchita Pal;Krishna Kumar - 通讯作者:
Krishna Kumar
Detection of Rotavirus from Hospitalized Diarrheic Children in Uttar Pradesh, India
印度北方邦住院腹泻儿童中检测到轮状病毒
- DOI:
10.1007/s12088-012-0279-6 - 发表时间:
2012 - 期刊:
- 影响因子:3
- 作者:
S. Dash;Krishna Kumar;Anuj Tewari;P. Varshney;A. Goel;A. K. Bhatia - 通讯作者:
A. K. Bhatia
Krishna Kumar的其他文献
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{{ truncateString('Krishna Kumar', 18)}}的其他基金
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 - 财政年份:2023
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
POSE: Phase I: Tuitus - A sustainable, inclusive, open ecosystem for Natural Hazards Engineering
POSE:第一阶段:Tuitus - 一个可持续、包容、开放的自然灾害工程生态系统
- 批准号:
2229702 - 财政年份:2022
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: Apparatus for Normalization and Systematic Control of the MOLLER Experiment
合作研究:莫勒实验标准化和系统控制装置
- 批准号:
2013142 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
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 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
The Impact of Federal Life Science Funding on University R&D
联邦生命科学资助对 R 大学的影响
- 批准号:
1064215 - 财政年份:2011
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Acquisition of a 500 MHz NMR Spectrometer
购买 500 MHz NMR 波谱仪
- 批准号:
0821508 - 财政年份:2008
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CAREER: Controlling Helix-Helix Interactions in Membrane Proteins
职业:控制膜蛋白中的螺旋-螺旋相互作用
- 批准号:
0236846 - 财政年份:2003
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant