arvindmohan / LSTM_ROM_Arxiv
A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269
☆41Updated 6 years ago
Alternatives and similar repositories for LSTM_ROM_Arxiv:
Users that are interested in LSTM_ROM_Arxiv are comparing it to the libraries listed below
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Updated 4 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
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆29Updated 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
- 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
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆23Updated 3 years ago
- Numerical tool for Construction of Reduced-order models for fluid flows.☆30Updated 4 years ago
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network☆58Updated last year
- ☆24Updated 6 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
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆16Updated last year
- Deep Learning for Reduced Order Modelling☆88Updated 3 years ago
- Uncertainty Quantification of RANS Data-Driven Turbulence Modeling☆56Updated 4 years ago
- Prediction of turbulent heat transfer using convolutional neural networks (CNNs)☆20Updated 2 years ago
- ☆62Updated 5 years ago
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆15Updated 3 years ago
- Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow …☆17Updated 3 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆21Updated last year
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 4 years ago
- Deep learning framework for model reduction of dynamical systems☆21Updated 4 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆19Updated last year
- DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling☆34Updated last year
- Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data☆35Updated 6 years ago
- Nonlinear proper orthogonal decomposition for convection-dominated flows☆13Updated 3 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆91Updated 5 years ago
- Robust active flow control over a range of Reynolds numbers using artificial neural network trained through deep reinforcement learning☆32Updated 4 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆44Updated 2 years ago
- Tensor Basis Neural Network for Scalar Mixing☆10Updated last year