I'm a master's graduate in Computational Science and Engineering from the Techincal University of Munich (TUM). Currently I am working as a research assistant in physics-based deep learning in the group of Prof. Thuerey. My interests are scientific computing, HPC, numerical simulations, and machine learning. As a mechanical engineer, my first love is fluid dynamics.
1. Fluidchen
A finite differences solver for incompressible Navier-Stokes equations (in 2D) with heat transfer included. It contains own implementations of linear solvers: relaxation methods and Krylov subspace methods, is parallelized using MPI, and handles arbitrary geometries of the domain boundary as well as obstacles. This work was pursued in a team of 3 towards the Computational Fluid Dynamics Lab pratical course at TUM.
This is my contribution to the open-source coupling library preCICE, which I wrote as a research assistant at the Chair of Scientific Computing at TUM. The PR is currently on hold due to changes in the software design and other recent decisions.
An intuitive web app that transcribes spoken input using whisper and gives summaries using the OpenAI API.
I’m currently deepening my knowledge of cloud technologies, while exploring ML for scientific computing.
Feel free to reach out through:
- LinkedIn: kanishk-bhatia