26 citations · 26 across the 4 of their papers we have counts for
4 papers
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…
A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
Tarek Iraki, Lukas Morand, Johannes Dornheim +2
The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed t…
Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization
Johannes Dornheim, Lukas Morand, Samuel Zeitvogel +3
A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this pap…