  Simulations using machine learning (ML) models and mechanistic models are
often run to inform decision-making processes. Uncertainty estimates of
simulation results are critical to the decision-making process because
simulation results of specific scenarios may have wide, but unspecified,
confidence bounds that may impact subsequent analyses and decisions. The
objective of this work is to develop a protocol to assess uncertainties in
predictions of machine learning and mechanistic simulation models. The protocol
will outline an uncertainty quantification workflow that may be used to
establish credible bounds of predictability on computed quantities of interest
and to assess model sufficiency. The protocol identifies key sources of
uncertainties in machine learning and mechanistic modeling, defines applicable
methods of uncertainty propagation for these sources, and includes
statistically rational estimators for output uncertainties. The work applies
the protocol to test cases relevant to the gas distribution industry and
presents learnings from its application. The paper concludes with a brief
discussion outlining a pathway to the wider adoption of uncertainty
quantification within the industry
