sayin / Physics_informed_GANs_turbulent_flows
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.
☆23Updated 4 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
- Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flow☆18Updated 2 years ago
- This repository contains the simple source codes of "Convolutional neural network and long short-term memory based reduced order surrogat…☆13Updated 4 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆23Updated last year
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆15Updated 3 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆25Updated 3 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆30Updated 2 years ago
- Deep Learning based method to try and learn the problem of inverse Navier Stokes and model the flow for an oscillating airfoil.☆20Updated 4 years ago
- Prediction of the velocity flow fields at a given distance from wall, starting from wall-measured quantities in wall-bounded turbulence☆20Updated 3 years ago
- POD-PINN code and manuscript☆49Updated 4 months ago
- This repository contains code for data-driven LES of two-dimensional turbulence.☆11Updated 3 years ago
- ☆19Updated 7 years ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆16Updated 2 years ago
- ☆9Updated last year
- Discontinuity Computing Using Physics-Informed Neural Network☆23Updated 11 months ago
- One-dimensional unsteady compressible reacting flow simulation framework, designed for simple prototyping and testing of novel reduced-or…☆27Updated last year
- Use deep learning to learn a turbulence model from high fedelity data. The model can reasonably predict other turbulent flows.☆19Updated 6 years ago
- Data preprocess method on Physics-informed neural networks☆13Updated last month
- Physics-informed neural networks for highly compressible flows 🧠🌊☆25Updated last year
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago
- Numerical tool for Construction of Reduced-order models for fluid flows.☆30Updated 4 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆29Updated last year
- Direct Numerical Simulation of Fluid Flow with IBM Using Python☆29Updated last year
- In computational fluid dynamics (CFD), the SIMPLE algorithm is a widely used numerical procedure to solve the Navier–Stokes equations. SI…☆15Updated 4 years ago
- This repository contains the codes for DNS of channel flow.☆9Updated 3 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆18Updated 2 years ago
- Python code to calculate proper orthogonal decomposition modes (aka principal components), which are then used to generate reduced order …☆34Updated 9 years ago
- LES-ML closures for Kraichnan turbulence☆18Updated 5 years ago
- Tensor Basis Neural Network for Scalar Mixing☆10Updated 2 years ago
- Companion code for Data-Driven Resolvent Analysis☆19Updated 3 years ago
- CNN model to predict the lift-drag ratio of airfoil☆15Updated 4 years ago