Hi, Welcome to Harshith Gowrachari’s website

Bio

I am a Computational Scientist with a background in Applied Mathematics and Aerospace Engineering, specializing in Advanced Aerodynamics and Propulsion. My work combines computational fluid dynamics (CFD), numerical modeling, high-fidelity simulations, high-performance computing (HPC), reduced order methods, and machine learning to tackle complex problems in aerospace, metallurgy, and energy sectors.

Research Interests

My research focuses on Computational Fluid Dynamics, Reduced Order Modelling, and Scientific Machine Learning.

I am particularly interested in reactive and multiphase flows, heat transfer, aeroacoustics, and aerodynamics, with applications ranging from academic studies to industrial problems in aerospace, metal industries, and energy, and more recently in climate physics. My work is grounded in the numerical approximation of partial differential equations (PDEs), with experience in discretization methods such as the Finite Volume, Finite Element, and Finite Difference methods.

I am also focused on reduced order methods, including projection-based and data-driven approaches, as well as non-linear model reduction techniques for the construction of efficient and accurate reduced order models, and on exploring Scientific Machine Learning to accelerate simulations and enhance predictive modelling.