Harder, H., &; Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.

Althoff, S. (2023). Predicting the Bond Quality of Heavy Copper Wire Bonds using a Friction Model Approach (Vol. 15). Shaker.

Qudus, U., Röder, M., Kirrane, S., &; Ngomo, A.-C. N. (2023). TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs. In T. R. Payne, V. Presutti, G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &; J. Li (Eds.), The Semantic Web – ISWC 2023 (Vol. 14265, pp. 465–483). Springer, Cham.https://doi.org/10.1007/978-3-031-47240-4_25 

Kersting, J., Maoro, F., &; Geierhos, M. (2023). Towards comparable ratings: Exploring bias in German physician reviews. Data &; Knowledge Engineering, 148, Article 102235.https://doi.org/10.1016/j.datak.2023.102235 

Bender, A., &; Sextro, W. (2021). Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties. In P. Do, S. King, &;https://doi.org/10.36001/phme.2021.v6i1.2843 __Olga Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1).https://doi.org/10.36001/phme.2021.v6i1.2843  

Schroder, D., Lange, S., Hangmann, C., &; Hedayat, C. (2020). Far-field prediction combining simulations with near-field measurements for EMI assessment of PCBs. In Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis (1st ed., pp. 315-346 (32)). Croyton, UK:https://doi.org/10.1049/pbcs072e_ch14__The Institution of Engineering and Technology (IET).https://doi.org/10.1049/pbcs072e_ch14 

Aimiyekagbon, O. K., Bender, A., &; Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. PHM Society European Conference, 5(1).

Bender, A., Schinke, L., &; Sextro, W. (2019). Remaining useful lifetime prediction based on adaptive failure thresholds. In M. Beer &; E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL2019) (Issue 29, pp. 1262–1269).

Sievers, S., Klobucnik, J., &; Miersch, D. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.https://doi.org/10.2139/ssrn.2237757 

Klobucnik, J., Miersch, D., &; Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic Journal.

Sprock, C., &; Sextro, W. (2014). Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending. In Instrumentation and Measurement Technology Conference (MTC) Proceedings, 2014 IEEE International (pp. 320–324).https://doi.org/10.1109/I2MTC.2014.6860760 

Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., &; Trautmann, H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII, 147–160.https://doi.org/10.1145/2460239.2460253 

Sievers, S., &; Mokwa, C. F. (2012). The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments.https://doi.org/10.2139/ssrn.2100501 

Hunstig, M., Hemsel, T., &; Sextro, W. (2012). An efficient simulation technique for high-frequency piezoelectric inertia motors. In Ultrasonics Symposium (IUS), 2012 IEEE International (pp. 277–280).https://doi.org/10.1109/ULTSYM.2012.0068 

Mokwa, C. F., &; Sievers, S. (2012). The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments. SSRN Electronic Journal.https://doi.org/10.2139/ssrn.2100501