2023[ to top ]
  • On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool. Engelhardt, Anna; Decke, Jens; Meier, David; Dulig, Franz; Ragunathan, Rishan; Wegener, Thomas; Sick, Bernhard; Niendorf, Thomas. In Advanced Engineering Materials, 25(21), bl 2300876. Wiley, 2023.
  • Dataset of a parameterized U-bend flow for deep learning application. Decke, Jens; Wünsch, Olaf; Sick, Bernhard. In Data in Brief, 50(1), bl 109477. 2023.
  • DADO – Low-Cost Query Strategies for Deep Active Design Optimization. Decke, Jens; Gruhl, Christian; Rauch, Lukas; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA). 2023.
2022[ to top ]
  • Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Decke, Jens; Engelhardt, Anna; Rauch, Lukas; Degener, Sebastian; Sajjadifar, Seyedvahid; Scharifi, Emad; Steinhoff, Kurt; Niendorf, Thomas; Sick, Bernhard. In Crystals, 9(12), bll 1–19. MDPI, 2022.
  • NDNET: A Unified Framework for Anomaly and Novelty Detection. Decke, Jens; Schmeißing, Jörn; Botache, Diego; Bieshaar, Maarten; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), bll 197–210. Springer, 2022.