Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
arXiv:2603.30014v2 Announce Type: replace-cross 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 opti
The increasing complexity of scientific experiments, particularly in high-energy physics, and the rapid advancements in AI/ML necessitate sophisticated workflow management.
This development highlights the fusion of advanced AI with distributed computing, crucial for optimizing complex, computationally intensive design processes across various high-value industries beyond scientific research.
The ability to leverage AI-assisted, multi-objective optimization within robust distributed computing frameworks will significantly enhance efficiency and discovery rates in hardware design and complex systems management.
- · High-energy physics research
- · AI/ML developers
- · Distributed computing providers
- · Hardware design and optimization sectors
- · Traditional, manual optimization workflows
- · Less scalable computing infrastructure
Faster design and iteration cycles for complex hardware and scientific instrumentation.
Accelerated innovation in sectors requiring high-precision design and experimental optimization, leading to new material discoveries or industrial efficiencies.
Potential for sovereign AI initiatives to adopt similar scalable architectures for managing their own critical infrastructure development and optimization.
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Read at arXiv cs.AI