My research lies at the intersection of computational modelling, experimental mechanics, theoretical mechanics, and machine learning. Currently, my research focuses on developing robust, cost-effective, and accurate non-contact photogrammetry-based techniques for measuring kinematic fields such as displacements and strains. Accurate measurement of these quantities enhances the accuracy and robustness of the constitutive models, thereby enabling a better understanding of material behaviour. In parallel, my work advances a thermodynamically consistent crystal plasticity-based framework for the reliable prediction of fatigue life (both High- and low-cycle regimes) of critical structural components, such as turbine blades of jet engines. In addition, my research is directed towards the development of computationally efficient approaches for modelling fracture in brittle and quasi-brittle materials, such as concrete, as well as in composites.
With the increasing adoption of machine learning in mechanics, I emphasize its careful and physically consistent integration into modeling frameworks. My research explores physics-informed and graph-based neural network frameworks to enable real-time understanding and prediction of mechanical behaviour, addressing key limitations of conventional numerical methods in terms of computational cost and scalability. Broadly, my vision is to contribute to the development of robust, efficient, and scalable methodologies for understanding the behaviour of material and structural systems, supporting the nation’s goals of self-reliance, and ensuring the safety, durability, and optimal performance of critical infrastructures.