Chris Tennant
The paper envisions AI-native particle accelerators that operate autonomously with minimal human input, focusing on AI co-design from the outset to optimize performance and reliability.
This paper outlines a future where particle accelerators are designed to be fully autonomous, using artificial intelligence from the start. Instead of adding AI to existing systems, the authors propose creating new facilities where AI is integral to their design and operation. The goal is to maximize performance and reliability, allowing these complex systems to run with minimal human intervention. The paper identifies nine key areas of research needed to achieve this vision, including adaptive learning and digital twins.
