For a wide range of materials, such as, e.g., battery electrodes, alloys or bio-materials, the underlying microstructure strongly influences mechanical and transport processes, which-in turn are key for the functionality of these materials. Thus, methodology for an efficient microstructure optimization is required. During the last decades, stochastic as well as numerical modeling and, in particular, the combination of both turned out to be powerful for virtual materials testing.
Stochastic 3D microstructure modeling allows for the generation of so-called digital twins and, moreover, of a wide range of further virtual, but realistic microstructures on the computer, while numerical modeling enables for the simulation of mechanical and transport properties of those virtual microstructures. In doing so, process-microstructure-property relationships can be quantitatively investigated by means of simulation studies in an efficient way. Ideally, this approach leads to structuring recommendations for experimentalists dealing with the synthesis and manufacturing of materials possessing optimized microstructures.
This EUROMECH colloquium focuses on recent advances in the field of data-driven microstructure modeling and addresses a wide spectrum of materials and applications: porous, composite and polycrystalline media, mechanical and transport properties. Topics for presentations include, but are not limited to
- machine learning and stochastic microstructure modeling,
- generation of digital twins
- numerical simulation of effective properties
- AI-based methods for characterizing, predicting or optimizing mechanical and transport properties, as well as
- elucidating process-microstructure and microstructure-property relationships
Abstract submission: Please send title and abstract of your proposed contribution to the following mail-address: email@example.com.
Accomodation and breakfast (three nights): 330 €
Conference fee: 280 € (Euromech members) and 310 € (non-members)