Dr. Lukas Petrich

• 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.
• 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.