Meet the CCML Lab

• Ph.D., Mechanical Engineering, IIT Bombay, 2025
• M.Tech, Mechanical Engineering (Aerodynamics & Propulsion), IIT Guwahati, 2019
• B.E., Mechanical Engineering, Gujarat Technological University, 2017
  
  

Research Interests: Combustion chemistry, Machine learning, Detailed chemical kinetic mechanisms for energetic materials & alternative fuels, Physics-informed machine learning frameworks, Combustion processes, and flame dynamics

More about Dr. Jay 

Project: Geometry-Aware Physics-Enhanced Fourier Neural Operator for Turbulent Flow Prediction from Sparse Data 

Project: Geometry-Aware Physics-Enhanced Fourier Neural Operator for Turbulent Flow Prediction from Sparse Data 

Project: Surrogate Modeling for Rayleigh–B´ enard Convection via Neural Ordinary Differential Equations 

Project: Development of a Data-Driven Wall Model for Turbulent Boundary Layer using Wall-Resolved Simulations  

Project: An Integrated CFD–ML Framework for Automated Simulation and Dynamic Mode Decomposition of Unsteady Flows Using Ansys PyFluent 

Project: CFD Analysis of Flow Over ONERA M6 Wing: Validation and Comparison of Turbulence Models 

Project: Hybrid CFD-ML Framework for Laminar Flames with Neural ODEs Chemistry for Hydrogen-air Combustion

Project: Accelerating Combustion CFD Using Neural ODEs-Based Chemical Kinetics Models in OpenFOAM

Project: Neural Operator Surrogate for Parametric 1D Reaction–Diffusion Dynamics

Project: Hyperparameter Optimization of Physics-Guided Autoencoder Neural ODEs for Chemical Kinetics

Project: Simulation of Flashback Prediction in Hydrogen Enriched Gas Turbine  

Project: Validation and Performance Assessment of Turbulence Models for the Delville Shear Layer Test Case

Project: Computational Fluid Dynamics (CFD) Simulation and Principal Component Analysis of Hydrogen-based Energy System

Project: Computational Fluid Dynamics (CFD) Simulation and Investigation of Flow over Volvo Bluff Body using OpenFOAM

Project: Reynolds Averaged Navier Stokes (RANS) & Large Eddie Simulation (LES) of Backward Facing Step and Validation with Experimental Data

Project: Building ML Pipelines and Models to Reduce the Numerical Integration Cost of Chemical Kinetics