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C03 Model reduction and formalisation for increased efficiency of integrative simulation chains

Project Area

C03

Project Leader

Christian Hopmann

Cooperation Projects

A02 A03 B01 B03 B05 C01 C02 C04 C06 Z01
C03 Model reduction and formalisation for increased efficiency of integrative simulation chains
Summary
The DediGrad subproject C03 develops methods of model reduction and formalisation in order to make the wide spectrum of material–morphology combinations in graded materials efficiently usable for simulation and optimisation tasks. To this end, heterogeneous data sources – drawn from literature, analytical approaches, numerical simulations, and data augmentation – are brought together within a structured data space. The objective is to establish fast and robust prediction models for homogenised material properties that meet the needs of component and process design. In this way, the subproject provides the methodological basis for accelerating complex integrative simulation chains without losing the physical grounding of the models.
001 Objectives

Data-Driven Material Models

  • Investigation of suitable fast prediction methods for component and process optimisation
  • Establishment of data-driven models for local properties of continuously graded materials
  • Classification, formalisation, and parameterisation of heterogeneous microstructures
  • Use of a diverse data lake comprising literature, analytical, and simulation data
002 Scientific Questions

Efficient Property Prediction

  • How can the material properties of the broad combinatorial spectrum of possible material and morphology combinations be predicted rapidly and accurately?
  • How can heterogeneous and multiscale microstructures be clustered into suitable classes and formalised appropriately?
  • Which methods are best suited to using a diverse data lake for predicting homogenised material properties while balancing accuracy and computation time?

Related Project Members

Project-related publications

L. Lise, H. Celik, R. Dahlmann, Ch. Hopmann Machine Learning-Based Stiffness Modelling for the Development of Fibre-Reinforced Thermoplastic Components. In: Institut für Kunststoffverarbeitung (IKV) in Industrie und Handwerk an der RWTH Aachen (Hrsg.): Proceedings of the 33rd International Colloquium Plastics Technology, 2026, ISBN: 978-3-8191-0527-2 – https://www.dedigrad.de/en/publications/machine-learning-based-stiffness-modelling-for-the-development-of-fibre-reinforced-thermoplastic-components-in-institut-fur-kunststoffverarbeitung-ikv-in-industrie-und-handwerk-an-der-rwth-aachen/

Subprojects

C03

Contributors

Luiz Lise Hakan Celik Christian Hopmann

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