šš¼ Hello there, Iām Viknesh!
šØš»āš PhD Candidate in the Scientific Computing & Imaging (SCI) Institute and the Department of Mechanical Engineering at the University of Utah, advised by Dr. Amirhossein Arzani. I work at the intersection of Fluid Mechanics and Scientific Machine Learning, developing interpretable AI methods that integrate physics-based principles with classical numerical solvers to produce physically consistent spatio-temporal models of complex flows.
š¬ Research Interests: Scientific Machine Learning, Unsteady Aerodynamics, Computational Fluid Mechanics, Wildfire Dynamics, and Wind Tunnel Measurements.
š Educational Background: 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.
š See my CV here.
š¬ Academic Research
- š„ Wildfire Dynamics:Identified two new non-dimensional numbers governing the convection-diffusion-reaction wildfire combustion models for the first time. Leverages stable and unstable manifolds (LCS) derived from wind topology to improve fire prediction.
- š¤ Interpretable Machine Learning:Developed the ADAM-SINDy framework for non-linear dynamical system identification, avoiding prior system knowledge. Check out the CRUNCH Seminar Talk.
- š©ļø Pitching Airfoil:Identified upstream-convecting vortices (vortices advecting against the flow direction) on a pitching airfoil using Time-resolved PIV & Pressure measurements.
- šŖļø Flow Instabilities:Sensitivity of multiple Hopf bifurcations and critical Reynolds numbers in lid-driven cavity flow problems, noting the influence of numerical methods and grid resolution.
š For the complete list of my publications, visit my Google Scholar Profile.