Welcome to the First Mini-Workshop for the Genesis Phase I Grant, "Towards Self-Evolving, Physics-Informed Digital Twins of Ion Accelerators and Isotope Separators."
As the complexity of modern accelerator facilities and rare isotope separation systems continues to grow, traditional static modeling approaches face significant limitations in real-time operational fidelity and predictive control. This workshop convenes researchers and practitioners across beam physics, advanced data science, and control systems to establish the foundational frameworks for self-evolving digital twins.
By bridging rigorous physical principles with adaptive machine learning paradigms, this initiative aims to develop robust, autonomously updating simulation frameworks capable of mirroring and optimizing complex accelerator dynamics. Discussions will center on architectural requirements, physics-informed neural networks, real-time telemetry integration, and collaborative research milestones for Phase I.
