Dr. Lukas Petrich

Email address	lukas.petrich(at)uni-ulm.de
Phone	+49 (0)731/50-23590
Fax	+49 (0)731/50-23649
Mailing address	Room 1.43 Helmholtzstr. 18 89069 Ulm
Office hours	on appointment

Publications

Kirstein, T., Petrich, L., Purushottam Raj Purohit, R. R. P., Micha, J.-S. and Schmidt, V. (2023). CNN-based Laue spot morphology predictor for reliable crystallographic descriptor estimation. Materials 16(9), 3397.
Rieder, P., Petrich, L., Serrano-Munoz, I., Fernández, R., Bruno, G. and Schmidt, V. (2023). Statistical comparison of substructures in pure aluminum before and after creep deformation, based on EBSD image data. Microscopy and Microanalysis 29(6), 1889-1900.
Petrich, L., Lohrmann, G., Martin, F., Stoll, A. and Schmidt, V. (2022). Model-based scenario analysis for effective site-specific weed control on grassland sites. Computers and Electronics in Agriculture 202, 107332.
Petrich, L., Schmidt, V. and Stoll, A. (2022). Tractor-mounted implement with section control and small-scale robot: Monte Carlo-based scenario analysis for effective weed control in extensive grassland. In VDI Wissensforum GmbH (Ed.), LAND.TECHNIK 2022 (Vol. 2395, p. 285-292).
Furat, O., Petrich, L., Finegan, D., Diercks, D., Usseglio-Viretta, F., Smith, K. and Schmidt, V. (2021). Artificial generation of representative single Li-ion electrode particle architectures from microscopy data. npj Computational Materials 7, 105.
Petrich, L., Furat, O., Wang, M., Krill III, C. E. and Schmidt, V. (2021). Efficient fitting of 3D tessellations to curved polycrystalline grain boundaries. Frontiers in Materials 8, 760602.
Petrich, L., Stoll, A. and Schmidt, V. (2021). Detection of Senecio jacobaea in drone images, using a machine-learning approach. In T. Astor and I. Dzene (Eds.), Sensing - New Insights into Grassland Science and Practice (Vol. 26). Grassland Science in Europe.
Heller, L., Karafítová, I., Petrich, L., Pawlas, Z., Shayanfard, P., Beneš, V., Schmidt, V. and Šittner, P. (2020). Numerical microstructure model of NiTi wire reconstructed from 3D-XRD data. Modelling and Simulation in Materials Science and Engineering 28(5), 055007.
Kopeček, J., Staněk, J., Habr, S., Seitl, F., Petrich, L., Schmidt, V. and Beneš, V. (2020). Analysis of polycrystalline microstructure of AlMgSc alloy observed by 3D EBSD. Image Analysis & Stereology 39(1), 1-11.
Petrich, L., Lohrmann, G., Neumann, M., Martin, F., Frey, A., Stoll, A. and Schmidt, V. (2020). Detection of Colchicum autumnale in drone images, using a machine-learning approach. Precision Agriculture 21, 1291-1303.
Schnepf, A., Black, C. K., Couvreur, V., Delory, B. M., Doussan, C., Koch, A., Koch, T., Javaux, M., Landl, M., Leitner, D., Lobet, G., Mai, T. H., Meunier, F., Petrich, L., Postma, J. A., Priesack, E., Schmidt, V., Vanderborght, J., Vereecken, H. and Weber, M. (2020). Call for participation: collaborative benchmarking of functional-structural root architecture models. The case of root water uptake. Frontiers in Plant Science 11, 316.
Seitl, F., Petrich, L., Staněk, J., Krill III, C. E., Schmidt, V. and Beneš, V. (2020). Exploration of Gibbs-Laguerre tessellations for three-dimensional stochastic modeling. Methodology and Computing in Applied Probability 23(2), 669-693.
Furat, O., Wang, M., Neumann, M., Petrich, L., Weber, M., Krill III, C. E. and Schmidt, V. (2019). Machine learning techniques for the segmentation of tomographic image data of functional materials. Frontiers in Materials 6, 145.
Král, P., Staněk, J., Kunčická, L., Seitl, F., Petrich, L., Schmidt, V., Beneš, V. and Sklenička, V. (2019). Microstructure changes in HPT-processed copper occurring at room temperature. Materials Characterization 151, 602-611.
Petrich, L., Staněk, J., Wang, M., Westhoff, D., Heller, L., Šittner, P., Krill III, C. E., Beneš, V. and Schmidt, V. (2019). Reconstruction of grains in polycrystalline materials from incomplete data using Laguerre tessellation. Microscopy and Microanalysis 25(3), 743-752.
Schnepf, A., Huber, K., Landl, M., Meunier, F., Petrich, L. and Schmidt, V. (2018). Statistical characterization of the root system architecture model CRootBox. Vadose Zone Journal 17(1), 170212.
Westhoff, D., Kuchler, K., Feinauer, J., Petrich, L. and Schmidt, V. (2018). Analysis, Modeling and Simulation of Tomographic Image Data for the 3D Microstructure of Electrode Material in Lithium-Ion Batteries. Practical Metallography 55(3), 134-146.
Petrich, L., Westhoff, D., Feinauer, J., Finegan, D. P., Daemi, S. R., Shearing, P. R. and Schmidt, V. (2017). Crack detection in lithium-ion cells using machine learning. Computational Materials Science 136, 297-305.