| Deep reinforcement learning methods for structure-guided processing path optimization J Dornheim, L Morand, S Zeitvogel, T Iraki, N Link, D Helm Journal of Intelligent Manufacturing 33, 333-352, 2022 | 30 | 2022 |
| Efficient Exploration of Microstructure-Property Spaces via Active Learning L Morand, N Link, T Iraki, J Dornheim, D Helm Frontiers in Materials 8, 824441, 2022 | 14 | 2022 |
| A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties T Iraki, L Morand, J Dornheim, N Link, D Helm Journal of Intelligent Manufacturing 35 (4), 1887-1903, 2024 | 13 | 2024 |
| Machine learning for structure-guided materials and process design L Morand, T Iraki, J Dornheim, S Sandfeld, N Link, D Helm Materials & Design 248, 113453, 2024 | 4 | 2024 |
| Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics T Iraki, L Morand, N Link, S Sandfeld, D Helm Modelling and Simulation in Materials Science and Engineering 32 (5), 055016, 2024 | 2 | 2024 |
| Crystallographic texture-property data set originating from a simulated multi-step metal forming process L Morand, T Iraki, D Helm | 2 | 2023 |
| Artificially generated crystallographic textures of steel sheets and their corresponding properties calculated by a Taylor-type crystal plasticity model L Morand, T Iraki, J Dornheim, J Pagenkopf, D Helm | 2 | 2021 |
| Generative models for capturing and exploiting the influence of process conditions on process curves T Iraki, N Link Journal of Intelligent Manufacturing, 1-20, 2022 | 1 | 2022 |
| Inverse Process-Structure-Property Mapping N Link, T Iraki, J Dornheim Research Impact on Key Industries, 85, 0 | 1 | |
| Sets of exemplary microstructure-property data generated via active learning and numerical simulations L Morand, T Iraki, J Dornheim, N Link, D Helm | | 2021 |
