kakkapriyesh / AE-ConvLSTM-Flow-DynamicsLinks
This repo contains a PyTorch-based AE-ConvLSTM model for fluid flow prediction. It can forecast 5–10 time steps per forward pass and over 100 steps in rollout. The model is trained using both data-driven and physics-constrained methods. While Burgers' equation was learned successfully with PDE loss, unsteady Navier–Stokes training did not conver…
☆25Updated 4 months ago
Alternatives and similar repositories for AE-ConvLSTM-Flow-Dynamics
Users that are interested in AE-ConvLSTM-Flow-Dynamics are comparing it to the libraries listed below
Sorting:
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 8 months ago
- Python tools for non-intrusive reduced order modeling☆20Updated 6 months ago
- ☆27Updated last year
- Implementation of physics-informed PointNet (PIPN) for weakly-supervised learning of incompressible flows and thermal fields on irregular …☆11Updated 3 months ago
- POD-PINN code and manuscript☆53Updated 10 months ago
- Multi-head attention network for airfoil flow field prediction☆15Updated 3 years ago
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆31Updated 3 years ago
- Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow …☆19Updated 4 years ago
- Laminar flow prediction using graph neural networks☆31Updated 8 months ago
- ☆41Updated 3 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆16Updated 4 years ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆84Updated last year
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆18Updated 2 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
- Code for Rice et al. 2020 "Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forcea…☆36Updated 3 weeks ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆50Updated 2 years ago
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network☆69Updated 2 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆26Updated 4 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆27Updated 2 years ago
- An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based …☆26Updated 2 years ago
- Physics-encoded recurrent convolutional neural network☆46Updated 3 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆92Updated 2 years ago
- Data preprocess method on Physics-informed neural networks☆20Updated 7 months ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆37Updated 2 years ago
- ☆25Updated 11 months ago
- A kind of loss function based on Least Squares Weighted Residual method for computational solid mechanics☆56Updated 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
- ☆47Updated 3 years ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆53Updated last year