693.932 PEOPLE
| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Durmaz, Ali Riza
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2023Influence of Transformation Temperature on the High‐Cycle Fatigue Performance of Carbide‐Bearing and Carbide‐Free Bainitecitations
- 2023Materials fatigue prediction using graph neural networks on microstructure representationscitations
- 2023Microstructure quality control of steels using deep learning
- 2022Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation modelscitations
- 2022Addressing materials’ microstructure diversity using transfer learningcitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopycitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy
- 2022Using optically pumped magnetometers to identify initial damage in bulk material during fatigue testingcitations
- 2022Optically pumped magnetometer measuring fatigue-induced damage in steelcitations
- 2022Addressing materials' microstructure diversity using transfer learningcitations
- 2021A deep learning approach for complex microstructure inferencecitations
- 2021Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Datacitations
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