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Industry 4.0 targets the conversion of the traditional industries intointelligent ones through technological revolution. This revolution is onlypossible through innovation, optimization, interconnection, and rapiddecision-making capability. Numerical models are believed to be the keycomponents of Industry 4.0, facilitating quick decision-making throughsimulations instead of costly experiments. However, numerical investigation ofprecise, high-fidelity models for optimization or decision-making is usuallytime-consuming and computationally expensive. In such instances, data-drivensurrogate models are excellent substitutes for fast computational analysis andthe probabilistic prediction of the output parameter for new input parameters.The emergence of Internet of Things (IoT) and Machine Learning (ML) has madethe concept of surrogate modeling even more viable. However, these surrogatemodels contain intrinsic uncertainties, originate from modeling defects, orboth. These uncertainties, if not quantified and minimized, can produce askewed result. Therefore, proper implementation of uncertainty quantificationtechniques is crucial during optimization, cost reduction, or safetyenhancement processes analysis. This chapter begins with a brief overview ofthe concept of surrogate modeling, transfer learning, IoT and digital twins.After that, a detailed overview of uncertainties, uncertainty quantificationframeworks, and specifics of uncertainty quantification methodologies for asurrogate model linked to a digital twin is presented. Finally, the use ofuncertainty quantification approaches in the nuclear industry has beenaddressed.

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer  Learning with Uncertainty Quantification Incorporated into Digital Twin for  Nuclear System