School of Engineering
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Saman Farhangdoust
Postdoctoral Scholar, Aeronautics and Astronautics
BioDr. Saman Farhangdoust is pursuing the goal of using his interdisciplinary knowledge to advance the Smart City and Space concept and make a lasting impact on society. He enjoys venturing into new disciplines to combine cutting-edge technologies and develop novel solutions to today’s structural safety problems.
As a Postdoctoral Scholar at Stanford University, Saman works on multi-functional materials and smart structures with particular emphases on intelligent self-sensing diagnostics and integrated health management for space and aircraft structures.
Outside of his research at Stanford, Saman is collaborating with MIT Media Lab as a Technical Consultant and also with Boeing Research and Technology as a Research Consultant to advance sensing and structural health monitoring systems.
Saman is considered a talented young researcher who has made valuable multidisciplinary contributions at an international level. These research activities have led to more than 40 publications including journal articles, conference proceedings, a textbook, U.S. Patents, national reports and guidelines to date. -
Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures, James and Anna Marie Spilker Chair of the Department of Aeronautics and Astronautics and Professor of Mechanical Engineering and of Aeronautics and Astronautics
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design and analysis of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on the nonlinear aeroelasticity and flight dynamics of Micro Aerial Vehicles (MAVs) with flexible flapping wings and N+3 aircraft with High Aspect Ratio (HAR) wings, layout optimization and additive manufacturing of wing structures, supersonic inflatable aerodynamic decelerators for Mars landing, and the reliable automated carrier landing via model predictive control. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-physics problems, high-order embedded boundary methods, uncertainty quantification, probabilistic machine learning, and efficient projection-based model order reduction as well as other forms of physics-based machine learning for time-critical applications such as design, active control, and digital twins.
