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
- Data-driven Reynolds stress modeling with physics-informed machine learning☆94Updated 6 years ago
- Uncertainty Quantification of RANS Data-Driven Turbulence Modeling☆62Updated 4 years ago
- code for active flow control of flow around cynder using Deep Reinforcement Learning☆50Updated 3 years ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆20Updated 2 years ago
- DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling☆46Updated 2 years ago
- One-dimensional unsteady compressible reacting flow simulation framework, designed for simple prototyping and testing of novel reduced-or…☆29Updated last year
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- A Python package for spectral proper orthogonal decomposition (SPOD).☆109Updated last week
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆26Updated 4 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆32Updated 2 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…☆32Updated 4 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
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆63Updated 3 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 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 3 months ago
- Companion code for Data-Driven Resolvent Analysis☆20Updated 4 years ago
- ☆22Updated 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
- ☆31Updated 4 months ago
- Python scripts to run resolution of the Reynolds-Averaged-Navier-Stokes equations over NACA 4 and 5 digits airfoils.☆24Updated 8 months ago
- DAFI: Ensemble based data assimilation and field inversion, repository for internal development☆61Updated 2 months ago
- Easy Reduced Basis method☆87Updated 2 weeks ago
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network☆69Updated 2 years ago
- Physics-informed neural networks for highly compressible flows 🧠🌊☆27Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Deep reinforcement learning with OpenFOAM☆44Updated 4 months ago
- Pythonic spectral proper orthogonal decomposition☆43Updated 3 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆25Updated last year