The California Amplitudes series brings together researchers working on scattering amplitudes and related topics to exchange ideas and discuss recent developments in an informal, collaborative setting. Building on this tradition, the California AI Amplitudes School and Workshop will focus on helping the community understand and apply artificial intelligence and machine learning to theoretical physics, with scattering amplitudes providing concrete case studies.
AI and ML offer promising opportunities to accelerate calculations, identify mathematical structures, and develop new research workflows. Realizing this potential requires both practical experience and a clear understanding of the methods’ capabilities and limitations, including how to assess and validate their results. This school and workshop will combine foundational lectures, step-by-step research examples, and hands-on exercises to help participants develop that understanding, share best practices, and identify applications to their own research.
The lectures will be organized around three components:
- Foundations of machine learning: an introduction to basic methods.
- Research case studies: step-by-step examples in which ML has helped, or has strong potential to help, address problems in scattering amplitudes.
- AI-assisted coding and agentic workflows: best practices and practical examples, including "vibe coding."
These three components will form the basis of the first three days of lectures and discussion. A fourth day will be devoted to participant talks, particularly presentations on applications of AI/ML to scattering amplitudes.
Organizers: Zvi Bern, François Charton, Lance Dixon, Sven Harder, Callum Jones, Zhongbo Kang, Michael Ruf, David Shih, and Matthias Wilhelm.