Microstructure - Data Science
The Microstructure - Data Science research group focuses on the data-driven characterization and optimization of microstructures.
The research work focuses on the development of characterization methods for microstructures as well as the development and application of data-driven analysis tools that make the interplay between microstructural properties and macroscopic material behaviour visible.
In addition to large-scale simulations using the phase field method, generation algorithms for creating 3D microstructures with customized properties are used to generate digital twins of microstructures and process chains.
For this purpose, tools are developed on the basis of or with the integration of real microstructure images, which can, for example, generate open-pored membrane structures, grain structures or rock fillings realistically.
In cooperation with the Research Data Management research group, concepts for the highly automated handling and efficient evaluation of large data sets are also being developed and applied. The overarching goal of the research questions includes bridging length scales by identifying effective regularities and developing prediction models to support accelerated material design.
