Mohammad Silani | Engineering | Editorial Board Member

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Assoc Prof Dr. Mohammad Silani | Engineering | Editorial Board Member

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.

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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.

Sedighe Mirbolouk | Engineering | Editorial Board Member

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Dr. Sedighe Mirbolouk | Engineering | Editorial Board Member 

Iran National Science Foundation | Iran

Dr. Sedighe Mirbolouk is a dedicated postdoctoral researcher and advanced machine learning specialist with strong expertise in communication systems, data science, and artificial intelligence. She is affiliated with the Iran National Science Foundation and has built a diverse research portfolio spanning deep learning, wireless communication optimization, image processing, and intelligent sensing systems. Her technical proficiency covers a wide spectrum of tools and programming environments, including Python, MATLAB, LATEX, and advanced libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, SciPy, Pandas, and Matplotlib. With a strong theoretical foundation in data telecommunication networks, convex optimization, communication theory, and signal and image processing, she integrates computational intelligence with modern communication challenges. In her role as a Postdoctoral Researcher (2024–2025) at the Iran National Science Foundation, Dr. Mirbolouk focuses on cutting-edge topics in graph learning and federated learning, particularly designing machine learning approaches for predictive beamforming in Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems. Her work aims to improve efficiency, adaptability, and intelligence in next-generation wireless communication networks. Previously, she served as a Visiting Researcher (2022) at the University of Oulu in Finland, where she explored advanced deep reinforcement learning methods to enhance ISAC designs. These research experiences have positioned her at the frontier of combining AI with communication technologies. During her doctoral studies at the University of Urmia (2018–2021), Dr. Mirbolouk contributed significantly to satellite–UAV cooperative network optimization. She developed innovative solutions involving UAV selection and power allocation for CoMP-NOMA transmissions, introducing both Lagrangian and heuristic algorithms that advanced energy-efficient communication frameworks. Alongside communications research, she proposed image processing solutions such as fuzzy histogram weighting methods and contrast enhancement techniques. Her academic involvement includes teaching core engineering subjects—Digital Communication, Probability and Statistics, and Signals and Systems—and assisting courses on Stochastic Processes and Digital Signal Processing. Her work at the National Elite Foundation (2020–2022) expanded her portfolio into biomedical machine learning applications, where she designed systems for automatic breast cancer detection using histopathology images and cardiac arrhythmia recognition using ECG signals through deep learning approaches. Dr. Mirbolouk holds a Ph.D. in Electrical Engineering, with earlier B.Sc. and M.Sc. degrees from the University of Guilan, where she studied SAR radar Doppler ambiguity for moving targets. Her scholarly contributions include high-impact publications in journals such as IEEE Transactions on Vehicular Technology, Physical Communication, and Multimedia Tools and Applications. Collectively, her research reflects an outstanding integration of machine learning, optimization, sensing, and communication technologies.

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Featured Publications

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Ali, S. (2022). Relay selection and power allocation for energy efficiency maximization in hybrid satellite-UAV networks with CoMP-NOMA transmission. IEEE Transactions on Vehicular Technology, 71(5), 5087–5100.

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Choukali, M. A. (2021). A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications, 80(2), 2221–2241.

Mirbolouk, S., Choukali, M. A., Valizadeh, M., & Amirani, M. C. (2020). Relay selection for CoMP-NOMA transmission in satellite and UAV cooperative networks. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5.

Choukali, M. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2023). A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancement. Multimedia Tools and Applications, 82(18), 28345–28365.

Hussein, A. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2025). Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework. Scientific Reports, 15(1), 25071.

Choukali, M. A., Mirbolouk, S., Valizadeh, M., & Amirani, M. C. (2024). Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication. Physical Communication, 68, 102576.