Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
🤖 AI Summary
AI adoption in particle, nuclear, and astrophysics is hindered by computational resource scarcity, cross-disciplinary expertise gaps, and challenges in transitioning AI research to production. Method: This paper introduces the first interdisciplinary AI infrastructure roadmap co-developed by the JENA tripartite community—based on extensive community consultation—and integrating deep learning, high-performance computing (HPC), scientific workflow orchestration, and AI engineering principles to build a reproducible, verifiable, and scalable Physics-AI platform. It proposes a phased resource deployment strategy, a collaborative training framework, and a sustainable funding model. Contribution/Results: The roadmap delivers a five-year strategic framework featuring core infrastructure metrics, twelve priority actions, and multi-tiered funding recommendations. It has received joint endorsement from ECFA, NuPECC, and APPEC, and has been formally integrated into the European Collaborative AI Infrastructure (EuCAIF) implementation blueprint.
Application Category
📝 Abstract
Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.
Problem
Research questions and friction points this paper is trying to address.
Overcoming computational resource limitations in AI for physics.
Addressing expertise gaps in AI integration within physics research.
Facilitating transition from AI R&D to production in physics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep learning for data analysis and simulations
Community survey to guide AI integration strategy
Funding strategies to scale AI in physics
🔎 Similar Papers
No similar papers found.
