Collaborative Research: Frameworks: SINAPSE: Scalable Infrastructure for AI-coupled Predictive Simulation Enhancement

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $725,000 · view on nsf.gov ↗

Abstract

Many critical scientific challenges, from understanding complex diseases to designing innovative materials, rely on sophisticated computer simulations. However, scientists often encounter a "silicon ceiling," where current computational power restricts their ability to model these intricate real-world phenomena accurately enough to achieve major breakthroughs. The SINAPSE project directly addresses this issue by developing a powerful, open-source software toolkit that combines Artificial Intelligence (AI) with High-Performance Computing (HPC). This integration promises to enhance simulation capabilities, effectively offering significant orders-of-magnitude performance gains. SINAPSE will provide foundational software that benefits the broader AI-HPC research community, advancing the field itself. The project is also dedicated to supporting education and training for students in these cutting-edge computational methods, fostering the next generation of STEM professionals. By making advanced simulations more powerful and accessible, SINAPSE serves the national interest by driving innovation and enabling solutions to pressing scientific challenges. The project aims to overcome the "silicon ceiling" limiting complex simulations by developing a Scalable Infrastructure for AI-driven Predictive Simulation Enhancements (SINAPSE), delivering an open, sustainable Software Development Kit (SDK) that seamlessly couples Artificial Intelligence (AI) with High-Performance Computing (HPC)

Key facts

NSF award ID
2514141
Awardee
Princeton University (NJ)
SAM.gov UEI
NJ1YPQXQG7U5
PI
Andrew S Rosen
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Software Institutes
Estimated total
$725,000
Funds obligated
$725,000
Transaction type
Standard Grant
Period
10/01/2025 → 09/30/2028