We present an uncertainty-aware online ensemble method for handling non-stationary data streams in fusion science applications. The approach dynamically weights multiple models …
This paper addresses the challenge of continual learning in particle accelerators, where machine learning models must adapt to changing data distributions and system configurations …
This preprint presents a novel uncertainty-guided online ensemble learning approach for handling non-stationary data streams in fusion science. The method adaptively selects and …