Personal Description

I am a young researcher with Master’s degree in Computational Sciences and Engineering (Applied Mechanics) and a background in Mechanical Engineering. My expertise lies in Computational Fluid Dynamics (CFD), High-Performance Computing (HPC), and Scientific Machine Learning. I currently work as CFD Support Engineer, contributing to R&D and providing technical consultation for both academia and industry. I have been actively involved with the CFD Group at the Computational Aeronautics Lab (CAL, SINES) and Supercomputing Lab (SCL, SINES). My work focuses on exploring surrogate modelling strategies as an efficient alternatives to conventional CFD for flow field prediction, enabling rapid design exploration while ensuring physical consistency.

My current research direction centers on Physics-Informed Neural Networks (PINNs) and neural operators (PINOs) such as Fourier Neural Operators (FNOs) and Convolutional Neural Operators (CNOs) as next-generation CFD surrogates, These approaches are shaping hybrid frameworks that accelerate simulations, improve predictive accuracy, and enable more sustainable engineering design. I am also actively seeking PhD opportunities to further pursue research at the intersection of CFD, machine learning and high-performance computing.