Study of machine learning techniques for accelerating finite element simulations of Stokes flows

This project starts by studying the finite element method for the steady state Navier-Stokes equations, afterwards it is implemented in Matlab and optimized via Vectorization achieving up to 5000x speed-up in some calculations. Then a reduced order model is studied to decrease the computational time of performing different simulations with slight modifications to the input parameters. Finally, the results obtained are compared against an already tested FEM code, Kratos Multiphysics, and against literature, and the performance of the developed solver for the equations is analyzed. It has been observed that the results obtained with the present work’s solver are almost equal to those made by the reference alternatives.