Research Interest
My research interests lie in computational methods for physical systems, spanning computational mechanics, uncertainty quantification, and scientific machine learning.
- Stochastic Computational Mechanics: Stochastic finite element methods, random fields, Karhunen–Loève expansion, polynomial chaos, stochastic dynamical systems, and uncertainty propagation.
- Scientific Machine Learning: Physics-informed neural networks, neural operators (e.g., DeepONet, FNO), and data-driven methods for modeling dynamical systems and structural/system health monitoring.
- Computational Modeling and Inverse Problems: Reduced-order modeling, parameter identification, inverse problems, and digital twins for scientific and engineering applications.
My broader research interests lie in developing mathematical and computational methods for efficient simulation, prediction, and analysis of physical systems.