  Artificial Intelligence is poised to transform the design of complex,
large-scale detectors like the ePIC at the future Electron Ion Collider.
Featuring a central detector with additional detecting systems in the far
forward and far backward regions, the ePIC experiment incorporates numerous
design parameters and objectives, including performance, physics reach, and
cost, constrained by mechanical and geometric limits. This project aims to
develop a scalable, distributed AI-assisted detector design for the EIC
(AID(2)E), employing state-of-the-art multiobjective optimization to tackle
complex designs. Supported by the ePIC software stack and using Geant4
simulations, our approach benefits from transparent parameterization and
advanced AI features. The workflow leverages the PanDA and iDDS systems, used
in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and
sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector
simulations. Tailored enhancements to the PanDA system focus on usability,
scalability, automation, and monitoring. Ultimately, this project aims to
establish a robust design capability, apply a distributed AI-assisted workflow
to the ePIC detector, and extend its applications to the design of the second
detector (Detector-2) in the EIC, as well as to calibration and alignment
tasks. Additionally, we are developing advanced data science tools to
efficiently navigate the complex, multidimensional trade-offs identified
through this optimization process.
