Epistemic uncertainty is related to machine learning model incompleteness, and could in theory be reduced in light of more training observations and/or additional knowledge about the correct model. Aleatoric uncertainty is associated with noise or imprecision on measurements. The latter type better aligns with the kind of uncertainty Olympus seeks to reproduce in optimization benchmark experiments.