Key research themes
1. How is Artificial Intelligence reshaping the modeling and simulation of complex fluids?
This research area investigates applying AI and ML techniques to overcome computational challenges in modeling multiscale, nonlinear behaviors of complex fluids. AI-driven data analysis, surrogate modeling, and feature extraction enable efficient and accurate predictions, thus bridging the gap between experimental data, simulations, and theoretical models.
2. What are the rheological characteristics and phase behaviors of complex surfactant-water systems and gels under varying conditions?
This theme centers on experimentally and theoretically characterizing the complex rheology of surfactant-based complex fluids like gels, liquid crystals, and concentrated emulsions. Understanding phase transitions, yielding, viscoelasticity, and dynamic modulus variations with temperature, concentration, and structure informs the design and application of soft materials in industrial and biomedical contexts.
3. How do mesoscopic structures and multiscale correlations influence the viscoelastic response and flow properties of complex fluids?
This research domain explores the length- and time-dependent viscoelastic behavior of complex fluids arising from their internal mesoscopic structure and dynamic correlations. Using microrheology, theoretical modeling, and hydrodynamics, the focus is on how intermediate length scales govern subdominant responses, influencing bulk properties, including yielding, shear-banding, and phase coexistence.