Uncertainty guided online ensemble for non-stationary data streams in fusion science

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 weights ensemble members based on uncertainty estimates, enabling robust predictions across changing data distributions.

Under review at Engineering Applications of Artificial Intelligence