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

University Of Texas At Austin, Austin TX

Investigators

Abstract

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.