research overview
- 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.
