用AI从头设计全自动粒子加速器，提升性能与可靠性。
Toward a Fully Autonomous, AI-Native Particle Accelerator
- AI全程参与加速器结构、诊断与应用的联合优化。
- 提出九个关键技术方向，推动设施向AI原生演进。
- 适合加速器物理、AI交叉领域研究者参考。
本文提出一种自驱动粒子加速器的愿景，未来设施将通过人工智能协同设计，从初始阶段即实现加速器晶格、诊断系统与科学应用的联合优化，以最大化性能并支持自主运行。不同于对人工主导系统的后期加装AI，我们主张从零开始构建AI原生平台。文中列出九大关键研究方向：代理式控制架构、知识融合、自适应学习、数字孪生、健康监测、安全框架、模块化硬件设计、多模态数据融合及跨领域协作。该路线图旨在引导加速器界迈向由AI驱动的设计与运行新范式，实现前所未有的科学产出与系统可靠性。
原文摘要 · Abstract (English)
This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.
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