Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 18 Mar 2025.
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References · 7
- 2018Machine Learning in High Energy Physics Community White Papercited by 49
- 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
- 2025Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Modelscited by 14
- 2022FAIR for AI: An interdisciplinary and international community building perspectivecited by 13
- 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
- 2017HEPData: a repository for high energy physics datacited by 6
- 2024Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Communitycited by 3
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