sayin / Physics_informed_GANs_turbulent_flowsView external linksLinks
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.
☆23Jan 28, 2021Updated 5 years ago
Alternatives and similar repositories for Physics_informed_GANs_turbulent_flows
Users that are interested in Physics_informed_GANs_turbulent_flows are comparing it to the libraries listed below
Sorting:
- Model-Order Reduction Framework for Nek5000☆16Feb 4, 2026Updated last week
- Computational fluid dynamics with tensorflow☆47Sep 23, 2023Updated 2 years ago
- ☆19Jan 27, 2018Updated 8 years ago
- CNN model to predict the lift-drag ratio of airfoil☆18Feb 28, 2021Updated 4 years ago
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆17May 14, 2021Updated 4 years ago
- Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh …☆10Dec 2, 2022Updated 3 years ago
- Deep Learning based method to try and learn the problem of inverse Navier Stokes and model the flow for an oscillating airfoil.☆24Jun 7, 2020Updated 5 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆20Mar 23, 2023Updated 2 years ago
- Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used …☆35Feb 10, 2021Updated 5 years ago
- Application of Deep Learning to Computational Fluid Dynamics☆21Apr 26, 2019Updated 6 years ago
- KTH-FlowAI / Towards-extraction-of-orthogonal-and-parsimonious-non-linear-modes-from-turbulent-flows