Mehrzad Soltani

University of North Texas, Isfahan University of Technology

Papers

Total Citations

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About

Mehrzad Soltani is a researcher whose work bridges computational mechanics, machine learning, and aerospace engineering. His primary research areas include interfacial mechanics in composite materials, structural reliability, and the dynamics of tethered space systems. Soltani’s major contribution lies in pioneering data-driven machine learning models to characterize traction–separation relations and interfacial imperfections in composites—a critical advancement for evaluating structural integrity in applications ranging from vehicle structures to soft robotics and aerospace. His most cited work, “Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models” (2021), has garnered 28 citations, reflecting its impact on predictive modeling for composite interfaces. Additionally, his research on “Dynamic analysis and trajectory tracking of a tethered space robot” (2016, 16 citations) demonstrates his versatility in addressing complex space robotics challenges. Soltani’s integration of machine learning with classical mechanics offers innovative tools for designing safer, more reliable composite structures, making his work highly relevant for students and researchers in materials science, aerospace, and computational engineering.