sayin/Physics_informed_GANs_turbulent_flows sayin/Physics_informed_GANs_turbulent_flows

  • Stars
    star
    20
  • Rank 1,121,974 (Top 23 %)
  • Language
    Python
  • Created over 4 years ago
  • Updated almost 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
sayin/Physics_informed_GANs_turbulent_flows
github

sayin

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution (DNS) fields without solving NS equations numerically.

More Repositories

1

Data_Driven_Symbolic_Regression

Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used to identify physical process, numerical schemes, and LES subgrid scale (eddy viscosity) turbulence models.
Python
34
star
2

Pyhton_LES_solver_2D_decaying_trubulence

This repository contains python based LES solver (FDM/spectral) of 2D decaying turbulence.
Python
13
star
3

1D-finite-diffence-Euler-sover

1d_Finite_Difference_Euler_Solver
Fortran
2
star
4

Laplace_solver

Fortran
1
star
5

1D_gaseous_cooling_matlab

MATLAB
1
star
6

gauss_elimination

Fortran code for gauss elimination method
Fortran
1
star
7

Optimization_MATH_4553

Python code for Optimal Control for 1D Convection-Diffusion Equation
Python
1
star