Yaswanth’s research focuses on developing statistical and computational frameworks to understand how uncertainty in materials, geometry, and operating conditions affects the performance and long-term behaviour of engineering structures. Many critical assets, such as bridges, pipelines, and energy systems, operate under variable loads and imperfect conditions, yet conventional models often assume ideal geometries and deterministic behaviour. Yaswanth develops stochastic models that represent these imperfections in a physically grounded and computationally efficient way, enabling more reliable predictions of structural response and degradation.
His current work combines random field modelling with statistical finite element methods (statFEM) to infer hidden changes in shape or material properties from surface measurements. By treating discrepancies between model predictions and observed data as informative signals, his methods allow early detection of subtle geometric or material irregularities that are otherwise difficult or costly to identify. These approaches support applications such as structural health monitoring, damage detection, and predictive maintenance.
Yaswanth is also working to extend these techniques to more complex real-world structures where data is limited and inspection is challenging, including components in nuclear and energy systems. A key direction of his research is the development of predictive tools that track how microstructural features evolve over time, enabling forecasts of damage progression before it becomes critical. His long-term goal is to integrate these ideas into data-informed digital twins that remain accurate as infrastructure ages and to explore emerging computational methods, including quantum approaches for solving high-dimensional stochastic mechanics problems.
