Khorrami, M. S.; Mianroodi, J. R.; Svendsen, B.: Finite-deformation phase-field microelasticity with application to dislocation core and reaction modeling in fcc crystals. Journal of the Mechanics and Physics of Solids 164, 104897 (2022)
Gierden, C.; Kochmann, J.; Waimann, J.; Svendsen, B.; Reese, S.: A Review of FE-FFT-Based Two-Scale Methods for Computational Modeling of Microstructure Evolution and Macroscopic Material Behavior. Archives of Computational Methods in Engineering 29, pp. 4115 - 4135 (2022)
Gierden, C.; Waimann, J.; Svendsen, B.; Reese, S.: A geometrically adapted reduced set of frequencies for a FFT-based microstructure simulation. Computer Methods in Applied Mechanics and Engineering 386, 114131 (2021)
Gierden, C.; Waimann, J.; Svendsen, B.; Reese, S.: FFT-based simulation using a reduced set of frequencies adapted to the underlying microstructure. Computer Methods in Materials Science 21 (1), pp. 51 - 58 (2021)
Shanthraj, P.; Liu, C.; Akbarian, A.; Svendsen, B.; Raabe, D.: Multi-component chemo-mechanics based on transport relations for the chemical potential. Computer Methods in Applied Mechanics and Engineering 365, 113029 (2020)
Mianroodi, J. R.; Svendsen, B.: Effect of Twin Boundary Motion and Dislocation-Twin Interaction on Mechanical Behavior in Fcc Metals. Materials 13 (10), 2238 (2020)
Alipour, A.; Reese, S.; Svendsen, B.; Wulfinghoff, S.: A grain boundary model considering the grain misorientation within a geometrically nonlinear gradient-extended crystal viscoplasticity theory. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476 (2235), 20190581 (2020)
Svendsen, B.: Constitutive relations for polar continua based on statistical mechanics and spatial averaging. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476 (2233), 20190407 (2020)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.