The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design. We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.

翻译：生产与分布式分析（PanDA）系统最初为欧洲核子研究中心（CERN）大型强子对撞机（LHC）上的ATLAS实验而开发，现已演化为一个在分布式计算资源上协调大规模工作流的稳健平台。该系统与其智能分布式调度与编排（iDDS）组件协同，通过可扩展且灵活的工作流引擎支持AI/ML驱动的工作流。我们提出了一种面向探测器设计优化的AI辅助框架，该框架将多目标贝叶斯优化与PanDA-iDDS工作流引擎相结合，以协调跨异构资源的迭代仿真。该框架解决了现代探测器设计中固有的高维参数空间探索难题。我们通过基准测试问题以及针对电子-离子对撞机（EIC）的ePIC和dRICH探测器的实际研究对该框架进行了验证。结果表明，该框架在多目标优化中实现了更高的自动化程度、可扩展性和效率。本工作为AI驱动的探测器设计及其他计算密集型科学应用建立了一种灵活且可扩展的范式。