Följ
Akhil Thomas
Akhil Thomas
Researcher, Fraunhofer IWM
Verifierad e-postadress på iwm.fraunhofer.de
Titel
Citeras av
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År
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
462021
Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
A Thomas, AR Durmaz, T Straub, C Eberl
Materials 13 (15), 3298, 2020
192020
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
122022
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
52023
Ontology Modelling for Materials Science Experiments
M Alam, H Birkholz, D Dessı, C Eberl, H Fliegl, P Gumbsch, P von Hartrott, ...
42021
Microstructural damage dataset (pytorch geometric dataset)
AR Durmaz, A Thomas
12023
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
Messvorrichtung und Verfahren zum Erfassen einer magnetischen Eigenschaft einer mechanisch belasteten Probe
A Blug, G Laskin, P Koss, AR Durmaz, T Straub, A Thomas
2023
MaterialDigital Dataset
C Schweizer, A Thomas, P Hartrott, E Augenstein, H Oesterlin, J Lienhard, ...
https://publica. fraunhofer. de/handle/publica/300549, 2020
2020
Abschlussbericht zu" MaterialDigital"
C Schweizer, R Reichenbach, A Butz, J Lienhard, T Herrmann, ...
2020
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Artiklar 1–10