From scientific models to reliable decision making – RDM as a backbone for validated, FAIR, and reproducible engineering simulations

  • Engineering simulations play a central role in predicting system behavior and supporting design decisions. Their impact will increase due to the concepts of virtual lab, digital twins and predictive maintenance, where expensive prototyping is reduced with simulations or where real measurement data of the structures or parts are used in combination with simulation models to improve decision making. The credibility of a simulation model depends on the trust it earns within the scientific and engineering community. This trust can only be established through rigorous verification and validation. Verification and validation are inherently hierarchical processes. At the highest level, global approaches—such as standardized benchmarks and shared validation datasets—provide a common foundation for assessing model correctness and applicability across domains. These global resources enable regulatory bodies, industry, and research communities to evaluate whether models and theirEngineering simulations play a central role in predicting system behavior and supporting design decisions. Their impact will increase due to the concepts of virtual lab, digital twins and predictive maintenance, where expensive prototyping is reduced with simulations or where real measurement data of the structures or parts are used in combination with simulation models to improve decision making. The credibility of a simulation model depends on the trust it earns within the scientific and engineering community. This trust can only be established through rigorous verification and validation. Verification and validation are inherently hierarchical processes. At the highest level, global approaches—such as standardized benchmarks and shared validation datasets—provide a common foundation for assessing model correctness and applicability across domains. These global resources enable regulatory bodies, industry, and research communities to evaluate whether models and their implementations meet agreed-upon standards. Research Data Management (RDM) provides the infrastructure to support these processes by enabling formalized model definitions, structured validation datasets, and transparent verification procedures and complete provenance descriptions of the underlying workflows. Currently, many engineering models are published as descriptive text or embedded in specific software implementations, which hinders reproducibility and standardization. Numerical results presented in publications must be fully reproducible. For regulatory assessment and interoperability, models require software-agnostic, machine-readable definitions that include all information needed for implementation and execution, such as scope, assumptions, parameter identification procedures, and uncertainty bounds. Formal definitions alone do not guarantee consistency. Verification benchmarks are essential to confirm that different implementations of the same mathematical or numerical model produce equivalent results. Likewise, validation requires structured experimental data that is publicly available and documented according to community-agreed protocols for tests, measurements, and metadata. Robust calibration and uncertainty quantification frameworks must provide systematic, reproducible procedures for parameter estimation and uncertainty propagation, while the complete provenance of these steps has to be documented. Validation should extend beyond controlled laboratory conditions to diverse real-world scenarios. The presentation will explore practical strategies for achieving reproducibility and reusability in engineering simulations through structured workflows, provenance tracking, and standardized data management practices. It will address the integration of experimental data for validation, the role of verification benchmarks, uncertainty quantification frameworks, and tool-independent model descriptions—including VMAP for result exchange—within collaborative platforms that support joint projects and regulatory compliance.zeige mehrzeige weniger

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Metadaten
Autor*innen:Jörg F. UngerORCiD
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.7 Modellierung und Simulation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Experimental data; Reproducibility (workflows); Reusability (provenance tracking of simulation results); UQ; Verification and validation
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Green Intelligent Building
Veranstaltung:GAMM Activity Group on Research Software Engineering and Research Data Management in Mathematics & Mechanics (GAMM FA RSE&RDM)
Veranstaltungsort:Braunschweig, Germany
Beginndatum der Veranstaltung:04.12.2025
Enddatum der Veranstaltung:05.12.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:30.12.2025
Referierte Publikation:Nein
Eingeladener Vortrag (wissenschaftliche Konferenzen):Ja
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