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Titel:

EVALUATION OF MODEL BIAS IDENTIFICATION APPROACHES BASED ON BAYESIAN INFERENCE AND APPLICATIONS TO DIGITAL TWINS

Dokumenttyp:
Conference paper
Autor(en):
Arcones, Daniel Andrés; Weiser, Martin; Koutsourelakis, Faidon-Stelios; Unger, Jörg F.
Abstract:
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model par...     »
Stichworte:
Bayesian networks; Finite element method; Gaussian distribution; Inference engines; Inverse problems; Parameter estimation; Uncertainty analysis; Bayesian; Bayesian inference; Bayesian uncertainty quantification; Gaussian Processes; Identification approach; Model bias; Modeling parameters; Original model; Statistical finite element methods; Uncertainty quantifications; Gaussian noise (electronic)
Herausgeber:
M., Papadrakakis; V., Papadopoulos; G., Stefanou
Kongress / Zusatzinformationen:
Cited by: 0
Verlag / Institution:
National Technical University of Athens
Jahr:
2023
Sprache:
English
WWW:
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175873711&partnerID=40&md5=2bba84aa1380870b0c977ee88f54e8d1
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