  Industry 4.0 targets the conversion of the traditional industries into
intelligent ones through technological revolution. This revolution is only
possible through innovation, optimization, interconnection, and rapid
decision-making capability. Numerical models are believed to be the key
components of Industry 4.0, facilitating quick decision-making through
simulations instead of costly experiments. However, numerical investigation of
precise, high-fidelity models for optimization or decision-making is usually
time-consuming and computationally expensive. In such instances, data-driven
surrogate models are excellent substitutes for fast computational analysis and
the probabilistic prediction of the output parameter for new input parameters.
The emergence of Internet of Things (IoT) and Machine Learning (ML) has made
the concept of surrogate modeling even more viable. However, these surrogate
models contain intrinsic uncertainties, originate from modeling defects, or
both. These uncertainties, if not quantified and minimized, can produce a
skewed result. Therefore, proper implementation of uncertainty quantification
techniques is crucial during optimization, cost reduction, or safety
enhancement processes analysis. This chapter begins with a brief overview of
the concept of surrogate modeling, transfer learning, IoT and digital twins.
After that, a detailed overview of uncertainties, uncertainty quantification
frameworks, and specifics of uncertainty quantification methodologies for a
surrogate model linked to a digital twin is presented. Finally, the use of
uncertainty quantification approaches in the nuclear industry has been
addressed.
