Our work focuses on the analytical, numerical, and experimental investigation of nonlinear structural phenomena. We analyze complex bifurcation behaviors, internal resonances, and vibration instabilities in structural elements and rotating machinery, aiming to predict and mitigate critical dynamic failures.
This research domain explores the coupled interactions within multi-component engineering systems, such as marine propulsion shafting and large-scale ocean structures. We model high-dimensional system mechanics to understand collective dynamics, wave propagation, and energy transmission paths under irregular environmental loading.
We investigate the integration of advanced smart materials (such as piezoelectric and magnetorheological materials) into structural configurations. This research is targeted toward developing high-efficiency vibration isolation platforms, structural health monitoring (SHM) arrays, and innovative energy-harvesting technologies for marine and industrial applications.
DATA DRIVEN APPROACHES (MACHINE LEARNING AND AI)
This frontier bridges traditional solid mechanics with modern computational data science. We develop and deploy physics-informed machine learning models, surrogate optimization neural networks, and automated anomaly detection algorithms to solve complex inverse problems and enhance predictive maintenance frameworks.