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  <doc>
    <id>55575</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>22</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forwardmodel's accuracy</title>
    <abstract language="eng">Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.</abstract>
    <parentTitle language="eng">Computational mechanics</parentTitle>
    <identifier type="doi">10.1007/s00466-022-02214-6</identifier>
    <identifier type="issn">1432-0924</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-555755</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">26.08.2022</enrichment>
    <enrichment key="PaperofMonth">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Isabela Coelho Lima</author>
    <author>Annika Robens-Radermacher</author>
    <author>Thomas Titscher</author>
    <author>Daniel Kadoke</author>
    <author>P.-S. Koutsourelakis</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Variational inference</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proper generalized decomposition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Goal-oriented</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital twin</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random field</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/55575/2022_Coelho Lima_Bayesian inference for random field parameters with a goal....pdf</file>
  </doc>
  <doc>
    <id>61551</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>26</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>417</volume>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Chichester</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Model bias identification for Bayesian calibration of stochastic digital twins of bridges</title>
    <abstract language="eng">Simulation-based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must then be carefully implemented. When applied to complex structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aimed at inferring the model parameters often fail to compensate for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for these models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of these approaches into the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error, and provides further insight into the system by including additional pre-existing information without modifying the computational model.</abstract>
    <parentTitle language="eng">Applied stochastic models in business and industry</parentTitle>
    <identifier type="doi">10.1002/asmb.2897</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-615519</identifier>
    <identifier type="issn">1526-4025</identifier>
    <note>This work was supported by “C07 - Data driven model adaptation for identifying stochastic digital twins of bridges” from the Priority Program (SPP) 2388/1 “Hundred plus” of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - Project number 501811638.</note>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">11.11.2024</enrichment>
    <enrichment key="PaperofMonth">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Daniel Andrés Arcones</author>
    <author>M. Weiser</author>
    <author>P.-S. Koutsourelakis</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian process</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>KOH</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian updating</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital twins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty quantification</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <collection role="themenfelder" number="">Green Intelligent Building</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/61551/2024_Andres Arcones_Model Bias Identification for Bayesian Calibration of Stochastic.pdf</file>
  </doc>
  <doc>
    <id>61544</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>32</pageLast>
    <pageNumber/>
    <edition/>
    <issue>e20</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName>Cambridge University Press</publisherName>
    <publisherPlace>England</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties</title>
    <abstract language="eng">We propose a systematic design approach for the precast concrete industry to promote sustainable construction practices. By employing a holistic optimization procedure, we combine the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data are used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physicsbased models which more often than not, do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed approach to treat the design process as a workflow, learn the missing links from data/models, and finally perform global optimization using the workflow is transferable to several other materials, structural, and mechanical problems. In the present work, the efficacy of the method is exemplarily illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28 days according to the Eurocode, the demolding time as computed by a complex nonlinear finite element model, and the maximum temperature during the hydration.</abstract>
    <parentTitle language="eng">Data-Centric Engineering</parentTitle>
    <identifier type="doi">10.1017/dce.2024.18</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-615443</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">11.11.2024</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>A. Agrawal</author>
    <author>E. Tamsen</author>
    <author>Jörg F. Unger</author>
    <author>P-S Koutsourelakis</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Black-box optimization under uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mix design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Performance oriented design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Precast concrete</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probabilistic machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable material design</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <collection role="themenfelder" number="">Green Intelligent Building</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/61544/2024_Unger_from concrete mixture to....pdf</file>
  </doc>
</export-example>
