# Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

arXiv: https://arxiv.org/abs/2603.30014 · HTML: https://mlsanity.com/paper/2603.30014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider](https://mlsanity.com/paper/2205.09185/) (2022)
- [AI-Assisted Detector Design for the EIC (AID(2)E)](https://mlsanity.com/paper/2405.16279/) (2024)
- [An intelligent Data Delivery Service for and beyond the ATLAS experiment](https://mlsanity.com/paper/2103.00523/) (2021)
- [iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration](https://mlsanity.com/paper/2510.02930/) (2025)

## Abstract (arXiv)

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.