Mianroodi, J. R.; Shanthraj, P.; Svendsen, B.: Strongly versus weakly non-local dislocation transport and pile-up. 24th International Congress of Theoretical and Applied Mechanics, Montreal, Canada (2016)
Reese, S.; Kochmann, J.; Mianroodi, J. R.; Wulfinghoff, S.; Svendsen, B.: Two-scale FE-FFT phase-field-based computational modeling of bulk microstructural evolution and nanolaminates. 12th World Congress on Computational Mechanics, Seoul, South Korea (2016)
Mianroodi, J. R.; Shanthraj, P.; Svendsen, B.: Comparison of algorithms and solution methods for classic and phase-field-based periodic inhomogeneous elastostatics. ECCOMAS Congress 2016, Crete, Greece (2016)
Svendsen, B.; Mianroodi, J. R.: Atomistic and phase-field modelling of nanoscopic dislocation processes. Dislocation based Plasticity, Kloster Schöntal, Schöntal, Germany (2016)
Mianroodi, J. R.; Svendsen, B.: Periodic molecular dynamics modeling of dislocation-stacking fault interaction. GDRi CNRS MECANO General Meeting on the Mechanics of Nano-Objects, MPIE, Düsseldorf, Germany (2013)
Mianroodi, J. R.; Svendsen, B.: Molecular Dynamics-Based Modeling of Dislocation-Stacking Fault Interaction. 84th Annual Meeting of International Association of Applied Mathematics and Mechanics (GAMM), Novi Sad, Serbia (2013)
Mianroodi, J. R.; Svendsen, B.: Modeling and calculation of the stacking fault free energy of iron at high temperature. International Workshop Molecular Modeling and Simulation: Natural Science meets Engineering, Frankfurt a. M., Germany (2013)
Mianroodi, J. R.; Shanthraj, P.; Svendsen, B.: Comparison of Methods for Discontinuous and Smooth Inhomogeneous Elastostatics. 24th International Congress of Theoretical and Applied Mechanics, Montreal, Canada (2016)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.