Shakiba's research focuses on the physics-based modeling of soft materials and composites under coupled mechanical loading and extreme conditions. She develops physics, chemistry, and mechanics-based constitutive models and devises high-fidelity numerical approaches and mechanistic machine learning to tackle engineering challenges. Shakiba's long-term research goal is twofold. First, developing theoretical frameworks to understand advanced material responses under extreme multi-physics conditions. Second, integrating the theoretical framework with machine learning approaches as physics-based machine learning is key to creating true digital twins. This combination will enable us to design intelligent, sustainable, and multi-functional materials for Aerospace applications. Moreover, such improved physics, chemistry, mechanic, and data-based models allow us to address societal challenges such as manufacturing innovative and sustainable designs for extreme conditions, creating digital twins for efficient autonomy, and tackling plastic pollution.
ASEN 3112 - Structures
Teaches Mechanics of Materials methods of stress and deformation analysis applicable to the design and verification of aircraft and space structures. It offers an introduction to matrix and finite element methods for truss structures, and to mechanical vibrations.
ASEN 5519 - Selected Topics
Reflects upon specialized aspects of aerospace engineering sciences. Course content is indicated in the online Class Search. May be repeated up to 9 total credit hours. Recommended prerequisite: varies.