Continual learning for particle accelerators

This paper addresses the challenge of continual learning in particle accelerators, where machine learning models must adapt to changing data distributions and system configurations over time. We present novel strategies for model adaptation that maintain performance in non-stationary environments while avoiding catastrophic forgetting.

NeurIPS 2025 Machine Learning for Physical Science Workshop