Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
2026-03-31 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingArtificial Intelligence
AI summaryⓘ
The authors created a system to help design scientific detectors more efficiently by combining AI techniques with a powerful workflow manager originally used at CERN. Their system uses smart algorithms to explore many design options in parallel across different computers, speeding up the process of finding the best designs. They tested this approach on real detectors planned for the Electron-Ion Collider and found it worked well in handling complex tasks automatically and at large scale. This shows how AI can be used to improve scientific design workflows.
PanDA systemDistributed computingBayesian optimizationMulti-objective optimizationDetector designElectron-Ion ColliderAI/ML workflowsHigh-dimensional parameter spaceWorkflow engineSimulation
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
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 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.