This paper proposes a multi-species plasma modeling framework for predicting contamination removal in SRF cavities, integrating reduced-order PIC simulations with physics-informed neural operators for real-time prediction.
Key findings
The framework combines reduced-order PIC scheme, surface reaction network model, and neural operator surrogate for computational acceleration.
Enables predictive modeling of contamination removal rates and optimization of processing protocols.
Proposed framework addresses the gap in predictive capabilities for plasma-based SRF cavity remediation.
Limitations & open questions
The framework's predictive accuracy and computational efficiency need to be validated against experimental data.
Integration with experimental diagnostics and parameter space exploration are ongoing challenges.