4 papers
Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Ronak Shoghi, Lukas Morand, Dirk Helm +1
The constitutive behavior of materials is modeled through relationships between stress, strain, and possibly additional internal variables. This results in relatively high-dimensio…
Semantic orchestration and exploitation of material data: A dataspace solution demonstrated on steel and copper applications
Yoav Nahshon, Lukas Morand, Matthias Büschelberger +5
In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable know…
Accurate Distances Measures and Machine Learning of the Texture-Property Relation for Crystallographic Textures Represented by One-Point Statistics
Tarek Iraki, Lukas Morand, Norbert Link +2
The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In mate…
Machine learning for structure-guided materials and process design
Lukas Morand, Tarek Iraki, Johannes Dornheim +3
In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to ta…