  The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear
fuel proposed to be used for multiple modern nuclear technologies. Therefore,
characterizing its safety is vital for the reliable operation of nuclear
technologies. However, the TRISO fuel failure probabilities are small and the
computational model is time consuming to evaluate them using traditional Monte
Carlo-type approaches. In the paper, we present a multifidelity active learning
approach to efficiently estimate small failure probabilities given an expensive
computational model. Active learning suggests the next best training set for
optimal subsequent predictive performance and multifidelity modeling uses
cheaper low-fidelity models to approximate the high-fidelity model output.
After presenting the multifidelity active learning approach, we apply it to
efficiently predict TRISO failure probability and make comparisons to the
reference results.
