[2603.30014] Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.30014 (cs) [Submitted on 31 Mar 2026] Title:Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing Authors:Derek Anderson, Amit Bashyal, Markus Diefenthaler, Cristiano Fanelli, Wen Guan, Tanja Horn, Alex Jentsch Meifeng Lin, Tadashi Maeno, Kei Nagai, Hemalata Nayak, Connor Pecar, Karthik Suresh, Fang-Ying Tsai, Anselm Vossen, Tianle Wang, Torre Wenaus View a PDF of the paper titled Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing, by Derek Anderson and 15 other authors View PDF Abstract: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...