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  <doc>
    <id>61300</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>209</pageFirst>
    <pageLast>217</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Bundesanstalt für Materialforschung und -prüfung (BAM)</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation>Deutscher Ausschuss für Stahlbeton (DAfStb)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="deu">Von Messdaten zum optimierten Bauteil durch Kombination von Material- und Strukturdesign</title>
    <abstract language="deu">Die Entwicklung eines optimierten Designs für Bauwerke erfordert die Berücksichtigung sowohl des Materials als auch des Strukturdesigns. Ziel des Beitrages ist die Vorstellung eines Designprinzips basierend auf automatisierten Workflows, das am Beispiel eines Biegebalkendesign zur Reduzierung des Treibhauspotenzials vorgestellt wird. Eine ganzheitliche Optimierung berücksichtigt Material- und Strukturdesign. Es werden physikalische Modelle mit Ansätzen aus dem maschinellen Lernen kombiniert, die mit experimentellen Daten kalibriert bzw. trainiert werden. Eine besondere Bedeutung hat dabei die Berücksichtigung von Unsicherheiten.</abstract>
    <parentTitle language="deu">11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM - Beiträge zum 63. Forschungskolloquium Green Intelligent Building am 16. und 17. Oktober 2024</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:b43-613003</identifier>
    <identifier type="isbn">978-3-9818564-7-7</identifier>
    <enrichment key="eventName">11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building</enrichment>
    <enrichment key="eventPlace">Berlin, Germany</enrichment>
    <enrichment key="eventStart">16.10.2024</enrichment>
    <enrichment key="eventEnd">17.10.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Jörg F. Unger</author>
    <author>Erik Tamsen</author>
    <author>A. Agrawal</author>
    <author>P.-S. Koutsourelakis</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Materialdesign</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Strukturdesign</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="">Eigenverlag BAM</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/61300/4.5_Block_4_05_Unger.pdf</file>
  </doc>
  <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>
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    <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>
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    <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>
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    <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>
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    <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>
  <doc>
    <id>62252</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume/>
    <type>article</type>
    <publisherName>NDT.net GmbH &amp; Co. KG</publisherName>
    <publisherPlace>Mayen, Germany</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges</title>
    <abstract language="eng">Simulation-based digital twins of bridges have the potential not only to serve as monitoring devices of the current state of the structure but also to generate new knowledge through physical predictions that allow for better-informed decision-making. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will show that quantifying the extent of the uncertainties introduced through the models that lead to such discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. The inclusion of an explicit bias term will be applied to a representative demonstrator case based on the thermal response of the Nibelungenbrücke of Worms. The findings from this work are englobed in the initiative SPP 100+, whose main aim is the extension of the service life of structures, especially through the implementation of digital twins.</abstract>
    <parentTitle language="eng">Special Issue of Research and Review Journal of Nondestructive Testing (ReJNDT)</parentTitle>
    <identifier type="issn">2941-4989</identifier>
    <identifier type="doi">10.58286/30524</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-622522</identifier>
    <enrichment key="eventName">EWSHM 2024 11th European Workshop on Structural Health Monitoring</enrichment>
    <enrichment key="eventPlace">Potsdam, Germany</enrichment>
    <enrichment key="eventStart">10.06.2024</enrichment>
    <enrichment key="eventEnd">13.06.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Daniel Andrés Arcones</author>
    <author>M. Weise</author>
    <author>P-S. Koutsourelakis</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital Twins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model Bias</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SPP100+</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bridge Monitoring</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/62252/2024_Andres_Arcones_Bias_Identification_Approaches_for_Model_Updating_of_Simulation_Based.pdf</file>
  </doc>
  <doc>
    <id>63629</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2867</pageFirst>
    <pageLast>2873</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>The fib, Fédération international du béton</publisherName>
    <publisherPlace>Antibes, France</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantifying the uncertainty of predictive simulations in digital twins through the identification of model bias</title>
    <abstract language="eng">This work presents a novel approach to quantifying uncertainty in digital twin simulations by addressing model bias through embedded parameter distributions. Traditional Bayesian methods often underestimate uncertainty due to assumptions of model correctness. We propose a hierarchical Bayesian framework combined with Polynomial Chaos Expansion to better capture and propagate uncertainty. The methodology is validated on an analytical example and a real-world case involving thermal deformation predictions of the Nibelungen Bridge, demonstrating improved predictive accuracy and reliability.</abstract>
    <parentTitle language="eng">Collection of Accepted Papers - Concrete structures: extend lifetim, limit impacts</parentTitle>
    <enrichment key="eventName">fib Symposium 2025</enrichment>
    <enrichment key="eventPlace">Antibes, France</enrichment>
    <enrichment key="eventStart">16.06.2025</enrichment>
    <enrichment key="eventEnd">18.06.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
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    <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>Digital Twins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model Bias</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Predictive simulations</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantifying the uncertainty</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <collection role="themenfelder" number="">Green Intelligent Building</collection>
  </doc>
  <doc>
    <id>65272</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>37</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName>arXiv.org</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration</title>
    <abstract language="eng">A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction’s reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature observations.</abstract>
    <parentTitle language="eng">Computational Engineering, Finance, and Science (cs.CE)</parentTitle>
    <identifier type="doi">10.48550/arXiv.2410.12037</identifier>
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    <author>Daniel Andrés Arcones</author>
    <author>M. Weiser</author>
    <author>P-S. Koutsourelakis</author>
    <author>Jörg F. Unger</author>
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    <subject>
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      <type>uncontrolled</type>
      <value>Model updating</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantity of Interest</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
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    <collection role="themenfelder" number="">Infrastruktur</collection>
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    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="institutes" number="">7.7 Modellierung und Simulation</collection>
    <collection role="themenfelder" number="">Green Intelligent Building</collection>
    <collection role="unnumberedseries" number="">Preprints der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
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