Romit-Maulik / CAE_LSTM_ROMS
Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"
☆21Updated 4 years ago
Alternatives and similar repositories for CAE_LSTM_ROMS:
Users that are interested in CAE_LSTM_ROMS are comparing it to the libraries listed below
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆15Updated 3 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆16Updated last year
- A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269☆41Updated 6 years ago
- Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow …☆17Updated 3 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆23Updated 3 years ago
- POD-PINN code and manuscript☆47Updated 2 months ago
- This repository contains the simple source codes of "Convolutional neural network and long short-term memory based reduced order surrogat…☆13Updated 3 years ago
- Numerical tool for Construction of Reduced-order models for fluid flows.☆30Updated 3 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆19Updated last year
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆29Updated last year
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 3 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆29Updated 2 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆20Updated last year
- Python code to calculate proper orthogonal decomposition modes (aka principal components), which are then used to generate reduced order …☆32Updated 9 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆47Updated 4 years ago
- Exploit Auto-encoder for exploring and predict flow dynamic☆10Updated 5 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…☆26Updated 4 years ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆15Updated last year
- Data-driven Identification of 2D Partial Differential Equations using Extracted Physical Features☆11Updated 3 years ago
- Uncertainty Quantification in the POD-NN framework☆19Updated 4 years ago
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆9Updated last year
- Deep learning framework for model reduction of dynamical systems☆21Updated 4 years ago
- DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling☆33Updated last year
- Multi-fidelity reduced-order surrogate modeling☆17Updated last month
- Python tools for non-intrusive reduced order modeling☆17Updated 6 months ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆14Updated 2 years ago
- Python scripts to run resolution of the Reynolds-Averaged-Navier-Stokes equations over NACA 4 and 5 digits airfoils.☆19Updated last week
- Standardized Non-Intrusive Reduced Order Modeling☆12Updated 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.☆19Updated 4 years ago