A research overview

Our research focuses on the simulation and analysis of multi-scale multi-physics fluid flow problems that leverage high performance computing (HPC) platforms (upto millions of computing units). Specifically, the work involves:

  1. Development of HPC centric numerical methods and algorithms for solving partial differential equations.
  2. Design of machine learning methods to analyze and model data generated from simulations.
  3. Investigation of turbulent flow problems that arise in combustion systems, high-speed aerodynamics and environmental flows.

Our current research includes:

  1. A scalable asynchronous computing method to solve partial differential equations on massive supercomputers (numerical methods and high performance computing).
  2. Development of efficient simulation algorithms and workflows for fluid flow solvers (high performance computing).
  3. Simulations of turbulent combustion phenomena in gas turbine and scramjet engines (energy/aerospace).
  4. Development of distributed machine learning methods for prediction of anomalous/extereme events in scientific phenomena.