👨🏻🎓 PhD Candidate in the Scientific Computing & Imaging Institute and the Mechanical Engineering Department at The University of Utah, Salt Lake City, Utah, USA.
🔬 Research Interests: Scientific Machine Learning, Computational Fluid Mechanics, Unsteady Aerodynamics, and Wildfire Dynamics.
🎓 Education: I hold an M.S. in Aerospace Engineering with Aerodynamics major from IIT Kanpur, India, where I focused on both Computational and Experimental Aerodynamics, and a B.E. in Aeronautical Engineering from Anna University, Tamil Nadu, India, where I developed a strong interest in Aerodynamics and solving PDEs.
📚 See my CV here.
🔬 Academic Research
🤖 Scientific Machine Learning:
- DIfferentiable Autoencoding Neural Operator (DIANO) framework, enabling a coarse-grid interpretable latent space, by methodological integration of Autoencoders, Operator learning, and Differentiable PDE solvers.
- ADAM-SINDy, a differentiable optimization framework for identification of Parameterized Nonlinear Dynamical Systems. Check out the CRUNCH Talk.
🔥 Wildfire Dynamics:
- Identified two New Non-dimensional Numbers governing the convection-diffusion-reaction wildfire combustion models. Leverages stable and unstable manifolds (LCS) of wind topology to predict fire advection.
🛩️ Pitching Airfoil:
- Identified Upstream-convecting vortices (vortices advecting against the flow) on a pitching airfoil using Time-resolved PIV & Pressure measurements.
🌪️ Flow Instabilities:
- Multiple Hopf Bifurcations and critical Reynolds numbers in lid-driven cavity flow problems, noting the influence of numerical schemes and grid resolution.
📚 For the complete list of my publications, visit my Google Scholar Profile.
🖥️ Software and Simulations
Developed and optimized the following programs/solvers for scientific machine learning and fluid flow problems. If links are unavailable, the corresponding papers are still under review.