Willcox-Research-Group / ROM-OpInf-Combustion-2DLinks
Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.
☆33Updated 3 years ago
Alternatives and similar repositories for ROM-OpInf-Combustion-2D
Users that are interested in ROM-OpInf-Combustion-2D are comparing it to the libraries listed below
Sorting:
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 4 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆20Updated 2 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆95Updated 6 years ago
- POD-PINN code and manuscript☆54Updated 11 months ago
- Uncertainty Quantification of RANS Data-Driven Turbulence Modeling☆62Updated 4 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
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆64Updated 3 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- Physics-informed neural networks for highly compressible flows 🧠🌊☆27Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last month
- One-dimensional unsteady compressible reacting flow simulation framework, designed for simple prototyping and testing of novel reduced-or…☆29Updated last year
- DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling☆47Updated 2 years ago
- Python tools for non-intrusive reduced order modeling☆20Updated 6 months ago
- POD and DMD decomposition of data from fluid dynamics. This work has been produced during my internship at the von Karman Institute for F…☆32Updated 5 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆26Updated 4 years ago
- This code implements the Tensor Basis Neural Network (TBNN) as described in Ling et al. (Journal of Fluid Mechanics, 2016).☆42Updated 7 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆33Updated 2 years ago
- A Python package for spectral proper orthogonal decomposition (SPOD).☆112Updated last month
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆50Updated 2 years ago
- code for active flow control of flow around cynder using Deep Reinforcement Learning☆50Updated 3 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆26Updated last year
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Numerical tool for Construction of Reduced-order models for fluid flows.☆30Updated 4 years ago
- Multi-fidelity reduced-order surrogate modeling☆25Updated 4 months ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆56Updated 9 months ago
- LES-ML closures for Kraichnan turbulence☆19Updated 5 years ago
- Python code to calculate proper orthogonal decomposition modes (aka principal components), which are then used to generate reduced order …☆37Updated 10 years ago
- Control of 2D Rayleigh Benard Convection using Deep Reinforcement Learning with Tensorforce and Shenfun.☆20Updated 2 years ago