Uncertainty based online ensemble on non-stationary data for fusion science

We present an uncertainty-aware online ensemble method for handling non-stationary data streams in fusion science applications. The approach dynamically weights multiple models based on their predictive uncertainty, enabling robust performance as data distributions shift.

NeurIPS 2025 Machine Learning for Physical Science Workshop