| A deep learning approach for complex microstructure inference AR Durmaz, M Müller, B Lei, A Thomas, D Britz, EA Holm, C Eberl, ... Nature communications 12 (1), 6272, 2021 | 52 | 2021 |
| Automated quantitative analyses of fatigue-induced surface damage by deep learning A Thomas, AR Durmaz, T Straub, C Eberl Materials 13 (15), 3298, 2020 | 21 | 2020 |
| Addressing materials’ microstructure diversity using transfer learning A Goetz, AR Durmaz, M Müller, A Thomas, D Britz, P Kerfriden, C Eberl npj Computational Materials 8 (1), 27, 2022 | 16 | 2022 |
| Fatigue lifetime prediction with a validated micromechanical short crack model for the ferritic steel EN 1.4003 E Natkowski, AR Durmaz, P Sonnweber-Ribic, S Münstermann International Journal of Fatigue 152, 106418, 2021 | 14 | 2021 |
| Efficient experimental and data-centered workflow for microstructure-based fatigue data: towards a data basis for predictive AI models AR Durmaz, N Hadzic, T Straub, C Eberl, P Gumbsch Experimental Mechanics 61, 1489-1502, 2021 | 11 | 2021 |
| Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models AR Durmaz, E Natkowski, N Arnaudov, P Sonnweber-Ribic, S Weihe, ... International Journal of Fatigue 160, 106824, 2022 | 10 | 2022 |
| Optically pumped magnetometer measuring fatigue-induced damage in steel PA Koss, AR Durmaz, A Blug, G Laskin, OS Pawar, K Thiemann, A Bertz, ... Applied Sciences 12 (3), 1329, 2022 | 9 | 2022 |
| Materials fatigue prediction using graph neural networks on microstructure representations A Thomas, AR Durmaz, M Alam, P Gumbsch, H Sack, C Eberl Scientific Reports 13 (1), 12562, 2023 | 7 | 2023 |
| Addressing materials’ microstructure diversity using transfer learning. npj Comput A Goetz, AR Durmaz, M Muller, A Thomas, D Britz, P Kerfriden Mater 8 (1), 27, 2022 | 7 | 2022 |
| Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy BI Bachmann, M Müller, D Britz, AR Durmaz, M Ackermann, O Shchyglo, ... Frontiers in Materials 9, 1033505, 2022 | 6 | 2022 |
| Using optically pumped magnetometers to identify initial damage in bulk material during fatigue testing K Thiemann, A Blug, P Koss, A Durmaz, G Laskin, A Bertz, F Kühnemann, ... Quantum Technologies 2022 12133, 83-89, 2022 | 3 | 2022 |
| Microstructural damage dataset (pytorch geometric dataset) AR Durmaz, A Thomas | 2 | 2023 |
| Thermal characterization of epitaxial grown polycrystalline silicon R Liebchen, O Breitschädel, AR Durmaz, A Griesinger Thin Solid Films 606, 99-105, 2016 | 2 | 2016 |
| Microstructure quality control of steels using deep learning AR Durmaz, ST Potu, D Romich, JJ Möller, R Nützel Frontiers in Materials 10, 1222456, 2023 | 1 | 2023 |
| Measuring magneto-mechanical hysteresis during fatigue testing using optically pumped magnetometers A Blug, PA Koss, AR Durmaz, G Laskin, A Bertz, F Kühnemann, T Straub | 1 | 2021 |
| Experimental and Computational Micromechanical Fatigue Damage Initiation Data AR Durmaz, E Natkowski | 1 | 2021 |
| MaterioMiner-An ontology-based text mining dataset for extraction of process-structure-property entities AR Durmaz, A Thomas, L Mishra, R Niranjan Murthy, T Straub | | 2024 |
| Influence of Transformation Temperature on the High‐Cycle Fatigue Performance of Carbide‐Bearing and Carbide‐Free Bainite O Gulbay, M Ackermann, A Gramlich, AR Durmaz, I Steinbach, U Krupp steel research international 94 (12), 2300238, 2023 | | 2023 |
| Author Correction: Materials fatigue prediction using graph neural networks on microstructure representations A Thomas, AR Durmaz, M Alam, P Gumbsch, H Sack, C Eberl Scientific Reports 13 (1), 13598, 2023 | | 2023 |
| A6. 4-Diamond-Based Magnetic Widefield-Microscopy of Domain Patterns in Electric Steel S Philipp, M Feuerhelm, A Durmaz, T Straub, N Mathes, X Vidal, S Deldar, ... Lectures, 75-76, 2023 | | 2023 |
