107 citations · 122 across the 10 of their papers we have counts for
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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…
Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties
Binh Duong Nguyen, Pavlo Potapenko, Aytekin Dermici +3
Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, p…
Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
Kishan Govind, Daniela Oliveros, Antonin Dlouhy +2
Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still pose…
Statistical analysis of Discrete Dislocation Dynamics simulations: initial structures, cross-slip and microstructure evolution
Aytekin Demirci, Dominik Steinberger, Markus Stricker +3
Over the past decades, discrete dislocation dynamics simulations have been shown to reliably predict the evolution of dislocation microstructures for micrometer-sized metallic samp…