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  <doc>
    <id>52127</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>e202000063</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>20</volume>
    <type>article</type>
    <publisherName>Wiley Online Libary</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient identification of random fields coupling Bayesian inference and PGD reduced order model for damage localization</title>
    <abstract language="eng">One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.”</abstract>
    <parentTitle language="eng">Proceedings in Applied Mathematics &amp; Mechanics</parentTitle>
    <identifier type="doi">10.1002/pamm.202000063</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-521275</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Annika Robens-Radermacher</author>
    <author>Felix Held</author>
    <author>Isabela Coelho Lima</author>
    <author>Thomas Titscher</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model reduction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model updating</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proper generalized decomposition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random field</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Variational Bayesian Inference</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 im Netzwerk der BAM verfügbar ("Closed 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/52127/2021_Robens_Radermacher_Efficient identification of random fields .pdf</file>
  </doc>
  <doc>
    <id>59800</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>22</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>6</volume>
    <type>article</type>
    <publisherName>John Wiley &amp; Sons Ltd.</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">PGD in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency</title>
    <abstract language="eng">Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model.</abstract>
    <parentTitle language="eng">Engineering Reports</parentTitle>
    <identifier type="doi">10.1002/eng2.12887</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-598001</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">11.11.2024</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dominic Strobl</author>
    <author>Jörg F. Unger</author>
    <author>C. Ghnatios</author>
    <author>Annika Robens-Radermacher</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Additive manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mapping for unseparable load</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model order reduction (MOR)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermal transient problem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensitivity analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proper generalized decomposition (PGD)</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">7 Bauwerkssicherheit</collection>
    <collection role="themenfelder" number="">Material</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="">Materialdesign</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/59800/2024_Strobl_PGD in thermal transient problems with a moving heat.pdf</file>
  </doc>
  <doc>
    <id>58483</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>27</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>1</volume>
    <type>article</type>
    <publisherName/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation of tools for describing, reproducing and reusing scientific workflows</title>
    <abstract language="eng">In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing.&#13;
Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust.&#13;
In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSs were deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https://github.com/BAMresearch/NFDI4IngScientificWorkflowRequirements), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.</abstract>
    <parentTitle language="eng">ing.grid</parentTitle>
    <identifier type="doi">10.48694/inggrid.3726</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-584832</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Philipp Diercks</author>
    <author>Dennis Gläser</author>
    <author>Ontje Lünsdorf</author>
    <author>Michael Selzer</author>
    <author>Bernd Flemisch</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FAIR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reproducibility</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scientific workflows</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tool comparison</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Workflow management</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/58483/inggrid-3726-diercks.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>
  <doc>
    <id>61302</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>132</pageFirst>
    <pageLast>137</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>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Modellierung und Simulation von 3D Betondruck – Design- und Prozessoptimierung</title>
    <abstract language="deu">Der 3D Betondruck ermöglicht die effiziente und ressourcenschonendere Herstellung maßgeschneiderter Betonstrukturen. Trotz des großen Potentials gibt es bisher keine einheitlichen Normen und Standards für den sicheren und robusten 3D Betondruck. Der Beitrag zeigt das Potential numerischer Methoden und Modelle zur effizienten und ressourcenschonenden Design- und Prozessoptimierung des 3D Betondrucks. Dazu werden die aktuellen Forschungsarbeiten im Bereich der Entwicklung robuster und zuverlässiger numerischer Zwillinge, geeigneter Optimierungstools sowie digitaler Workflows diskutiert.</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="isbn">978-3-9818564-7-7</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-613023</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>
    <enrichment key="RelatedIdentifier">https://nbn-resolving.org/urn:nbn:de:kobv:b43-613383</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/61575</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Annika Robens-Radermacher</author>
    <author> Saif-Ur-Rehman</author>
    <author>Christoph Wolf</author>
    <author>Cezary Kujath</author>
    <author>Yuxiang He</author>
    <author>Alexander Mezhof</author>
    <author>Götz Hüsken</author>
    <author>Olubunmi Anthony Jeyifous</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Modellierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Simulation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>3D Betondruck</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Designoptimierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Prozessoptimierung</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/61302/3.2_Block_3_02_Robens.pdf</file>
  </doc>
  <doc>
    <id>61301</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>218</pageFirst>
    <pageLast>223</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>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Modell- und Parameterunsicherheiten am Beispiel eines digitalen Brückenzwillings</title>
    <abstract language="deu">Digitale Zwillinge bieten wertvolle Einblicke in das Verhalten von Bauwerken und ermöglichen eine fundierte Entscheidungsfindung. Durch den Einsatz von Simulationen, die auf physikalischen Gesetzen beruhen, ist es möglich, Vorhersagen über die Struktur auf der Grundlage zukünftiger oder hypothetischer Situationen zu treffen. Die Verwendung solcher Simulationen impliziert jedoch eine Reihe von Annahmen und Vereinfachungen, die unvermeidbare Fehler in die Vorhersagen einbringen. Die Quantifizierung dieser Unsicherheiten ist der Schlüssel für den Einsatz zuverlässiger digitaler Zwillinge von Brücken.</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:b43-613013</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>
    <enrichment key="RelatedIdentifier">https://nbn-resolving.org/urn:nbn:de:kobv:b43-613383</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/61568</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Daniel Andrés Arcones</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Modellunsicherheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Parameterunsicherheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Digitaler Brückenzwilling</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/61301/4.6_Block_4_06_Arcones.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>
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    <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>
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    <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>
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    <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>
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  <doc>
    <id>60842</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <title language="eng">From advanced simulation models to industrial applications</title>
    <abstract language="eng">The integration of advanced simulation models into industrial applications, especially in civil engineering, is a challenge that requires to develop new concepts. In particular, this is related to the validation of models using open access and FAIR data as well as objective methods for identifying model parameters and model comparison with the integration of both the models and their calibration into reproducible workflow systems. &#13;
The challenges are illustratively discussed for a variety of applications ranging from bridge monitoring over the structural behavior under blast loading up to the additive manufacturing of concrete. An important basis for model validation is the existence of machine-readable data structures for experimental data, e.g. based on semantic representations. In addition, platforms to share this structured data in a searchable format in addition with reproducible scientific workflows must be established.&#13;
Based on these tools, objective methods for model parameter estimation and validation of the models are a critical step in ensuring their accuracy and usefulness in industrial applications. Bayesian inference is one approach that can be used to optimize simulation parameters, improving the accuracy of the model while being able to estimate the uncertainty. Integrating these procedures in a real-time application with a bidirectional information exchange between the physical systems and the virtual simulation model leads to the concept of digital twins.&#13;
For complex simulation models, the computational efficiency is a significant barrier both for real-time applications and in the context of model calibration with potentially millions of required forward-model evaluations. As a consequence, reduced order modeling approaches such as the proper generalized decomposition method can be used to build an efficient to evaluate model - speeding up the online computation but requiring a precomputation in an offline phase.&#13;
Finally, a perspective for integrating material and structural design into a joint design optimization is given. The integration of advanced simulation models into industrial applications is a rapidly evolving field with significant potential for innovation and progress. From additive manufacturing of concrete to digital twins and reduced order modeling, there are many tools and approaches that can be used to improve industrial processes and drive progress in a variety of industries.</abstract>
    <enrichment key="eventName">Kolloquium des MUSEN-Zentrums Braunschweig</enrichment>
    <enrichment key="eventPlace">Braunschweig, Germany</enrichment>
    <enrichment key="eventStart">04.05.2023</enrichment>
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    <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>Reduced order models</value>
    </subject>
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  </doc>
  <doc>
    <id>51034</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>Article number: 29</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7</volume>
    <type>article</type>
    <publisherName>SpringerOpen</publisherName>
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    <title language="eng">Efficient structural reliability analysis by using a PGD model in an adaptive importance sampling schema</title>
    <abstract language="eng">One of the most important goals in civil engineering is to guarantee the safety of the construction. Standards prescribe a required failure probability in the order of 10−4 to 10−6. Generally, it is not possible to compute the failure probability analytically.&#13;
Therefore, many approximation methods have been developed to estimate the failure probability. Nevertheless, these methods still require a large number of evaluations of the investigated structure, usually finite element (FE) simulations, making full probabilistic design studies not feasible for relevant applications. The aim of this paper is to increase the efficiency of structural reliability analysis by means of reduced order models. The developed method paves the way for using full probabilistic approaches in industrial applications. In the proposed PGD reliability analysis, the solution of the structural computation is directly obtained from evaluating the PGD solution for a specific parameter set without computing a full FE simulation. Additionally, an adaptive importance sampling scheme is used to minimize the total number of required samples. The accuracy of the failure probability depends on the accuracy of the PGD model (mainly influenced on mesh discretization and mode truncation) as well as the number of samples in the sampling algorithm. Therefore, a general iterative PGD reliability procedure is developed to automatically verify the accuracy of the computed failure probability. It is based on a goal-oriented refinement of the PGD model around the adaptively approximated design point. The methodology is applied and evaluated for 1D and 2D examples. The computational savings compared to the method based on a FE model is shown and the influence of the accuracy of the PGD model on the failure probability is studied.</abstract>
    <parentTitle language="eng">Advanced Modeling and Simulation in Engineering Sciences</parentTitle>
    <identifier type="doi">10.1186/s40323-020-00168-z</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-510345</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Annika Robens-Radermacher</author>
    <author>Jörg F. Unger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reliability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Probability of failure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adaptive importance sampling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reduced order models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proper Generalized Decomposition</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/51034/2020_Robens-Radermacher_Efficient structural reliability analysis.pdf</file>
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    <id>60040</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>12</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName>Cornell University</publisherName>
    <publisherPlace>Ithaca, NY</publisherPlace>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Adaptive Gaussian process regression for Bayesian inverse problems</title>
    <abstract language="eng">We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.</abstract>
    <parentTitle language="eng">arXiv.org</parentTitle>
    <identifier type="doi">10.48550/arXiv.2404.19459</identifier>
    <identifier type="issn">2331-8422</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-600408</identifier>
    <enrichment key="eventName">Algoritmy 2024 - Central-European Conference on Scientific Computing</enrichment>
    <enrichment key="eventPlace">Vysoké Tatry, Slovakia</enrichment>
    <enrichment key="eventStart">15.03.2024</enrichment>
    <enrichment key="eventEnd">20.03.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://doi.org/10.5281/zenodo.11066159</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>P. Villani</author>
    <author>Jörg F. Unger</author>
    <author>M. Weiser</author>
    <subject>
      <language>eng</language>
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      <value>Active learning</value>
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    <subject>
      <language>eng</language>
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      <value>Bayesian inverse problems</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian Process Regression (GPR)</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parameter identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Surrogate models</value>
    </subject>
    <collection role="ddc" number="62">Ingenieurwissenschaften</collection>
    <collection role="ddc" number="624">Ingenieurbau</collection>
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    <collection role="literaturgattung" number="">Graue Literatur</collection>
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    <collection role="themenfelder" number="">Materialdesign</collection>
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    <collection role="unnumberedseries" number="">Preprints der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/60040/Adaptive Gaussian Process Regression for Bayesian inverse problems_Unger_Weiser.pdf</file>
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