Simulations play an important role in the continuing effort to reduce development time and risks. However, large and complex models are necessary to accurately simulate the dynamic behavior of complex engineering systems. In recent years, the use of data-driven models based on machine learning (ML) algorithms has become popular for predicting the structural dynamic behavior of mechanical systems. Due to their advantages in capturing non-linear behavior and efficient calculation, data-driven models are used in a variety of fields like uncertainty quantification, optimization problems, and structural health monitoring. However, the black box structure of ML models reduces the interpretability of the results and complicates the decision-making process. Hierarchical Bayesian Networks (HBNs) offer a framework to combine expert knowledge with the advantages of ML algorithms. In general, Bayesian Networks (BNs) allow connecting inputs, parameters, outputs, and experimental data of various models to predict the overall system-level dynamic behavior. This characteristic of BNs enables a divide and conquer approach. Hence, complex engineering systems can be split into more easily describable subsystems. HBNs are an extension of BNs that can use knowledge about the structure of the data to introduce a bias that can contribute to improving the modelling result. In this work, an approach to design a HBN is presented where each model in the network can be a parametric reduced finite-element models. The influence of the hierarchical approach is evaluated by comparing a HBN and a BN of the model from the Sandia structural dynamics challenge.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hierarchical Bayesian Networks for Predicting the Structural Behavior of Coupled Systems


    Weitere Titelangaben:

    Sae Int. J. Adv. and Curr. Prac. in Mobility


    Beteiligte:
    Stoll, Georg (Autor:in) / Atzrodt, Heiko (Autor:in) / Schmidt, Hendrik (Autor:in) / Hülsebrock, Moritz (Autor:in)

    Kongress:

    12th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference ; 2022



    Erscheinungsdatum :

    2022-06-15


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Hierarchical Bayesian Networks for Predicting the Structural Behavior of Coupled Systems

    Hülsebrock, Moritz / Schmidt, Hendrik / Stoll, Georg et al. | British Library Conference Proceedings | 2022



    Test Resource Allocation in Hierarchical Systems Using Bayesian Networks

    Sankararaman, Shankar / McLemore, Kyle / Mahadevan, Sankaran et al. | AIAA | 2013


    Predicting traffic flow using Bayesian networks

    Castillo, Enrique / Menéndez, José María / Sánchez-Cambronero, Santos | Elsevier | 2007


    Predicting traffic flow using Bayesian networks

    Castillo, Enrique | Online Contents | 2008