Tom Kirstein

 

T. Kirstein, S. Aßmann, O. Furat, S. Will and V. Schmidt, Determination of droplet size from wide-angle light scattering image data using convolutional neural networks. In: Machine Learning: Science andTechnology 5, 2024. pdf

L. Fuchs, T. Kirstein, C. Mahr, O. Furat, V. Baric, A. Rosenauer, L. Mädler and V. Schmidt, Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data. Mach. Learn.: Sci. Technol. 5 (2024) 025007. pdf

T. Kirstein, L. Petrich, R. R. P. Purushottam Raj Purohit, J.-S. Micha and V. Schmidt,  CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation . In:  Materials,  2023.pdf 

O. Furat, T. Kirstein, T. Leißner, K. Bachmann, J. Gutzmer, U. A. Peuker and V. Schmidt,  Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas. In: Minerals Engineering 206,   2024. pdf 

S. Englisch, R. Ditscherlein, T. Kirstein, L. Hansen, O. Furat, D. Drobek, T. Leißner, B. A. Zubiri, A. P. Weber, V. Schmidt, U. A. Peuker and E. Spiecker,  3D analysis of equally X-ray attenuating mineralogical phases utilizing a correlative tomographic workflow across multiple length scales. In: Powder Technology 419,   2023. pdf

O. Furat, D. P. Finegan, Z. Yang, T. Kirstein, K. Smith and V. Schmidt,  Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks. In: npj Computational Materials 8,   2022. pdf

F. von Loeper, T. Kirstein, B. Idlbi, H. Ruf, G. Heilscher and V. Schmidt, Probabilistic analysis of solar power supply using D-vine copulas based on meteorological variables. In: S. Goettlich, M. Herty and A. Milde (eds.) Mathematical Modeling, Simulation and Optimization for Power Engineering and Management. Mathematics in Industry, vol 34. Springer, 2021, pp. 51-68.