Dr. Matthias Neumann

Publications

• G. Gaiselmann, M. Neumann, L. Holzer, T. Hocker, M. Prestat and V. Schmidt, Stochastic 3D modeling of LSC cathodes based on structural segmentation of FIB-SEM images. Computational Materials Science 67 (2013), 48–62.
• G. Gaiselmann, M. Neumann, O. Pecho, T. Hocker, V. Schmidt and L. Holzer, Quantitative relationships between microstructure and effective transport properties based on virtual materials testing. AIChE Journal 60 (2014), 1983–1999.
• O. M. Pecho, O. Stenzel, B. Iwanschitz, P. Gasser, M. Neumann, V. Schmidt, M. Prestat, T. Hocker, R.J. Flatt and L. Holzer, 3D microstructure of Ni-YSZ anodes: Prediction of effective transport properties and optimization of redox-stability. Materials 8 (2015), 5554–5585.
• M. Neumann, J. Staněk, O. Pecho, L. Holzer, V. Beneš and V. Schmidt, Stochastic 3D modeling of complex three-phase microstructures in SOFC-electrodes with completely connected phases. Computational Materials Science 118 (2016), 353-364.
• O. Stenzel, O.M. Pecho, L. Holzer, M. Neumann and V. Schmidt, Predicting effective conductivities based on geometric microstructure characteristics. AIChE Journal 62 (2016), 1834–1843.
• M. Neumann and V. Schmidt, Stochastic 3D modeling of amorphous microstructures: A powerful tool for virtual materials testing. Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (2016).
• T. Q. Luong, N. Erwin, M. Neumann, A. Schmidt, C. Loos, V. Schmidt, M. Fändrich and R. Winter, Hydrostatic pressure increases the catalytic activity of amyloid fibril enzymes. Angewandte Chemie International Edition 55 (2016), 12412–12416.
• O. Stenzel, M. Neumann, O. Pecho, L. Holzer and V. Schmidt, Big data for microstructure-property relationships: A case study of predicting effective conductivities. AIChE Journal 63 (2017), 4224–4232.
• S. H. Ibrahim, M. Neumann, F. Klingner, V. Schmidt and T. Wejrzanowski, Analysis of the 3D microstructure of tape-cast open-porous materials via a combination of experiments and modeling. Materials & Design 133 (2017), 216-233.
• M. Kulosa, M. Neumann, M. Boeff, G. Gaiselmann, V. Schmidt and A. Hartmaier, A study on microstructural parameters for the characterization of granular porous ceramics using a combination of stochastic and mechanical modeling. International Journal of Applied Mechanics 9 (2017), 1750069.
• W. Close, M. Neumann, A. Schmidt, M. Hora, K. Annamalai, M. Schmidt, B. Reif, V. Schmidt, N. Grigorieff and M. Fändrich, Physical basis of amyloid fibril polymorphism. Nature Communications 9 (2018), 699.
• M. Neumann, O. Furat, D. Hlushkou, U. Tallarek, L. Holzer and V. Schmidt, On microstructure-property relationships derived by virtual materials testing with an emphasis on effective conductivity. In: M. Baum, G. Brenner, J. Grabowski, T. Hanschke, S. Hartmann, and A. Schöbel (eds.), Simulation Science: Proceedings of the Clausthal-Göttingen International Workshop on Simulation Science, Göttingen 2017. Communications in Computer and Information Science (CIS), Springer 2018, pp. 145-158.
• E. Machado Charry, M. Neumann, J. Lahti, R. Schennach, V. Schmidt and K. Zojer, Pore space extraction and characterization of sack paper using micro-CT. Journal of Microscopy 272 (2018), 35-46.
• M. Neumann, M. Osenberg, A. Hilger, D. Franzen, T. Turek, I. Manke, V. Schmidt, On a pluri-Gaussian model for three-phase microstructures, with applications to 3D image data of gas-diffusion electrodes. Computational Materials Science 156 (2019), 325-331.
• M. Neumann, B. Abdallah, L. Holzer, F. Willot and V. Schmidt, Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study. Transport in Porous Media 128 (2019), 179-200.
• M. Neumann, R. Cabiscol, M. Osenberg, H. Markötter, I. Manke, J.-H. Finke and V. Schmidt, Characterization of the 3D microstructure of Ibuprofen tablets by means of synchrotron tomography. Journal of Microscopy 274 (2019), 102-113.
• O. Furat, M.Y. Wang, M. Neumann, L. Petrich, M. Weber, C.E. Krill III and V. Schmidt, Machine learning techniques for the segmentation of tomographic image data of functional materials. Frontiers in Materials 6 (2019), 145.
• M. Neumann, A. Wagner, N. Bohn, M. Osenberg, A. Hilger, I. Manke, J. Binder and V. Schmidt, Characterization of hierarchically structured electrodes with different thicknesses by means of experiments and image analysis. Materials Characterization 155 (2019), 109778.
• M. Neumann, C. Hirsch, J. Staněk, V. Beneš and V. Schmidt, Estimation of geodesic tortuosity and constrictivity in stationary random closed sets. Scandinavian Journal of Statistics 46 (2019), 848-884.
• M. Neumann, O. Stenzel, F. Willot, L. Holzer and V. Schmidt, Quantifying the influence of microstructure on effective conductivity and permeability: virtual materials testing. International Journal of Solids and Structures 184 (2020), 211-220.
• M. Weber, A. Bäuerle, M. Schmidt, M. Neumann, M. Fändrich, T. Ropinski and V. Schmidt, Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning. Journal of Microscopy 277 (2020), 12-22.
• M. Neumann, E. Machado Charry, K. Zojer and V. Schmidt, On variability and interdependence of local porosity and local tortuosity in porous materials: a case study for sack paper. Methodology and Computing in Applied Probability, in print.
• L. Petrich, G. Lohrmann, M. Neumann, F. Martin, A. Frey, A. Stoll and V. Schmidt, Detection of Colchicum autumnale in drone images, using a machine-learning approach. Precision Agriculture, in print.
• B. Prifling, M. Neumann, D. Hlushkou, C. Kübel, U. Tallarek and V. Schmidt, Generating digital twins of mesoporous silica by graph-based stochastic microstructure modeling. Computational Materials Science, in print.

Teaching (Exercise Sessions)

Semester	Course
SS 2016	Stochastik I
WS 2015/16	Elementare Wahrscheinlichkeitsrechnung und Statistik
SS 2015	Räumliche Statistik
WS 2014/15	Stochastics II
SS 2014	Methods of Monte Carlo Simulation II