About me

I am a graduate student in Mechanical and Aerospace engineering at Oklahoma State University. My doctoral adviser is Dr. Omer San, PI at Computational Fluid Dynamics Laboratory at OSU. Our lab’s work is centered around application of machine learning algorithms in conjuction with physics-based model to computational physics problems.

My research is aimed at addressing the question of how data-driven machine learning algorithms can help us in improving the accuracy and computational performance of challenging problems in fluid mechanics such as turbulence closure. My past research experience has helped me to address this question by developing computational frameworks that use deep learning algorithms for reduce order modeling of nonlinear fluid flows. My primary research in turbulence closure modeling is still at the incipient stage and our preliminary study shows the potential of a deep learning algorithm for subgrid-scale model closure in LES. We are eagerly pursuing this topic in the actual deployment of deep learning algorithms in CFD numerical codes.

My research goal is to use the big data generated from numerical simulations, experiments, sensors data and integrate it with a physics-based model to build computationally efficient frameworks for studying real-life problems. The prior information about the governing laws of the physical system can be incorporated into machine learning algorithms to make them interpretable and physically consistent and I am interested in building such hybrid frameworks for computational physics applications.