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.
☆34Updated 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:
- Python tools for non-intrusive reduced order modeling☆21Updated this week
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆29Updated 4 years ago
- Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow …☆23Updated 4 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆96Updated 6 years ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆21Updated 3 years ago
- Companion code for Data-Driven Resolvent Analysis☆24Updated 4 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆27Updated last year
- Uncertainty Quantification of RANS Data-Driven Turbulence Modeling☆62Updated 5 years ago
- Python code to calculate proper orthogonal decomposition modes (aka principal components), which are then used to generate reduced order …☆39Updated 10 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆20Updated 2 years ago
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 5 years ago
- POD-PINN code and manuscript☆57Updated last year
- One-dimensional unsteady compressible reacting flow simulation framework, designed for simple prototyping and testing of novel reduced-or…☆30Updated 2 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆37Updated 2 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆67Updated 3 years 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…☆34Updated 5 years ago
- A Python package for spectral proper orthogonal decomposition (SPOD).☆116Updated 3 weeks ago
- Multi-fidelity reduced-order surrogate modeling☆31Updated 7 months ago
- Numerical tool for Construction of Reduced-order models for fluid flows.☆31Updated 5 years ago
- DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling☆50Updated 2 years ago
- Direct Numerical Simulation of Fluid Flow with IBM Using Python☆33Updated 2 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆25Updated 2 years ago
- ☆25Updated 5 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- Physics-informed neural networks for highly compressible flows 🧠🌊☆29Updated 2 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆37Updated 2 months ago
- Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flow☆23Updated 3 years ago
- Python scripts to run resolution of the Reynolds-Averaged-Navier-Stokes equations over NACA 4 and 5 digits airfoils.☆24Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago