Isfahan University of Technology | Iran
Dr. Mohammad Silani is an accomplished Associate Professor in the Department of Mechanical Engineering at Isfahan University of Technology (IUT), Iran, where he currently serves as the Head of International Scientific Cooperation. His academic career reflects a continuous trajectory of excellence in multiscale modeling, computational mechanics, and advanced materials research. Since joining IUT as an Assistant Professor in 2015, Dr. Silani has made significant contributions in micromechanics, multiscale simulation, fracture mechanics, and computational materials science. From 2022 to 2023, he was awarded the prestigious MSCA Seal of Excellence Fellowship at the Free University of Bozen-Bolzano, Italy, where he advanced adaptive concurrent multiscale methods for wear modeling and developed coarse-grained molecular dynamics tools for fatigue crack propagation. His international research engagements also include visiting fellowships at the University of New South Wales in Australia, Qatar University, the National University of Singapore, and multiple research positions at Bauhaus University Weimar, Germany, where he contributed to the development of open-source multiscale finite element codes and advanced modeling techniques for nanocomposites. Dr. Silani earned all three of his degrees—B.Sc., M.Sc., and Ph.D.—from IUT, specializing in solid mechanics, fracture mechanics, vibrations, FEM, and multiscale analysis. He possesses strong programming expertise in Python, Fortran, MATLAB, and Abaqus scripting and has advanced proficiency in leading finite element software including Abaqus, ANSYS, and LS-DYNA. His research achievements include more than 2,400 citations with an H-index of 20, reflecting his impactful contributions to computational mechanics, phase-field modeling, stochastic analysis, XFEM, SBFEM, and machine-learning-assisted material design. He has supervised over 70 postgraduate students, taught a wide range of undergraduate and graduate courses, and reviewed for leading journals such as Materials & Design, International Journal of Fatigue, Composite Structures, and Scientific Reports. His extensive publication record includes high-impact works in Advanced Materials, International Journal of Fracture, Computational Mechanics, Nanotechnology, Acta Mechanica Sinica, and Journal of Mechanical Behavior of Biomedical Materials. Dr. Silani’s honors include the Distinguished Young Professor Award from Iran’s National Elites Foundation (2022, 2023), multiple national science grants, a DAAD Research Grant, and project funding from the German Research Foundation (DFG). His current research spans phase-field modeling of nanowires, fracture and wear simulations, machine learning for materials design, nano- and micro-scale damage analysis, bone tissue mechanics, and Industry 4.0-based mechanical monitoring. Dr. Silani maintains active collaborations with leading researchers worldwide, reinforcing his position as a distinguished scholar in computational mechanics and multiscale material modeling.
Featured Publications
A computational library for multiscale modeling of material failure
Talebi, H., Silani, M., Bordas, S. P. A., Kerfriden, P., & Rabczuk, T. (2014). A computational library for multiscale modeling of material failure. Computational Mechanics, 53(5), 1047–1071.
Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions
Hamdia, K. M., Silani, M., Zhuang, X., He, P., & Rabczuk, T. (2017). Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 206(2), 215–227.
First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials
Mortazavi, B., Silani, M., Podryabinkin, E. V., Rabczuk, T., Zhuang, X., & Shapeev, A. V. (2021). First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials. Advanced Materials, 33(35), 2102807.