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New Bayesian calibration method fuses heterogeneous data for dynamic systems

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel multi-level Bayesian calibration method designed to fuse information from various sources and account for uncertainties in dynamic multi-component systems. The approach quantifies uncertainty at both component and system levels, enabling corrected model predictions. It processes heterogeneous data sequentially through an iterative strategy, applicable in both offline and real-time online calibration scenarios. The effectiveness of this methodology is demonstrated using thermo-mechanical behavior analysis of gas turbine engine rotor blades. AI

IMPACT This research could improve the accuracy of complex system modeling and prediction in engineering applications.

RANK_REASON The item is an academic paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New Bayesian calibration method fuses heterogeneous data for dynamic systems

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The item is an academic paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Berkcan Kapusuzoglu, Sankaran Mahadevan, Shunsaku Matsumoto, Yoshitomo Miyagi, Daigo Watanabe ·

    Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model

    arXiv:2608.18430v1 Announce Type: cross Abstract: This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for time-dependent multi-component systems. The develope…

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