为粒子物理等领域制定AI基础设施发展路线，破解算力与人才瓶颈。
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
- 基于社区调研提出分阶段的AI基础设施建设方案。
- 明确未来五年需优先投入算力、培训与资金支持以推动落地。
- 适合关注科研数字化转型的物理学家与机构决策者参考。
人工智能正在重塑科学探索，深度学习在粒子、核物理及天体物理的数据分析、模拟和信号探测中发挥核心作用。尽管在JENA共同体（ECFA、NuPECC、APPEC）及欧陆先进计算与智能基金（EuCAIF）推动下，AI集成进展显著，但广泛采用仍受限于算力不足、专业人才短缺以及从研发到生产转化困难等问题。本文基于社区调研，提出战略性路线图，明确关键基础设施需求，优先推进培训计划，并建议资金支持策略，旨在未来五年内全面提升基础物理学领域的AI能力。
原文摘要 · Abstract (English)
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.
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