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Nowadays, interest in combining mathematical knowledge about phenomena anddata from the physical system is growing. Past research was devoted todeveloping so-called high-fidelity models, intending to make them able to catchmost of the physical phenomena occurring in the system. Nevertheless, modelswill always be affected by uncertainties related, for example, to theparameters and inevitably limited by the underlying simplifying hypotheses on,for example, geometry and mathematical equations; thus, in a way, there existsan upper threshold of model performance. Now, research in many engineeringsectors also focuses on the so-called data-driven modelling, which aims atextracting information from available data to combine it with the mathematicalmodel. Focusing on the nuclear field, interest in this approach is also relatedto the Multi-Physics modelling of nuclear reactors. Due to the multiple physicsinvolved and their mutual and complex interactions, developing accurate andstable models both from the physical and numerical point of view remains achallenging task despite the advancements in computational hardware andsoftware, and combining the available mathematical model with data can furtherimprove the performance and the accuracy of the former.  This work investigates this aspect by applying two Data-Driven Reduced OrderModelling (DDROM) techniques, the Generalised Empirical Interpolation Methodand the Parametrised-Background Data-Weak formulation, to literature benchmarknuclear case studies. The main goal of this work is to assess the possibilityof using data to perform model bias correction, that is, verifying thereliability of DDROM approaches in improving the model performance and accuracythrough the information provided by the data. The obtained numerical resultsare promising, foreseeing further investigation of the DDROM approach tonuclear industrial cases.

Multi-Physics Model Bias Correction with Data-Driven Reduced Order Modelling Techniques: Application to Nuclear Case Studies