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The developed methodology has two elements: quantifying the uncertainty at component and system levels, by fusing all available information, and corrected model prediction. A multi-level Bayesian calibration approach is developed to estimate component-level and system-level parameters using measurement data that are obtained at different time instances for different system components. Such heterogeneous data are consumed in a sequential manner, and an iterative strategy is developed to calibrate the parameters at the two levels. This calibration strategy is implemented for two scenarios: offline and online. The offline calibration uses data that is collected over all the time-steps, whereas online calibration is performed in real-time as new measurements are obtained at each time-step. Analysis models and observation data for the thermo-mechanical behavior of gas turbine engine rotor blades are used to analyze the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1115\/1.4055315","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T06:19:15Z","timestamp":1661235555000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model"],"prefix":"10.1115","volume":"23","author":[{"given":"Berkcan","family":"Kapusuzoglu","sequence":"first","affiliation":[{"name":"Vanderbilt University Department of Civil, and Environmental Engineering, , Nashville, TN 37235"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sankaran","family":"Mahadevan","sequence":"additional","affiliation":[{"name":"Vanderbilt University Department of Civil, and Environmental Engineering, , Nashville, TN 37235"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsaku","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Mitsubishi Heavy Industries, Ltd. 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