echowve / phygnnetLinks
Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"
☆50Updated 2 years ago
Alternatives and similar repositories for phygnnet
Users that are interested in phygnnet are comparing it to the libraries listed below
Sorting:
- PDE Preserved Neural Network☆56Updated 4 months ago
- MIONet: Learning multiple-input operators via tensor product☆38Updated 2 years ago
- ☆60Updated last month
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- ☆54Updated 3 years ago
- Modified Meshgraphnets with more features☆54Updated 8 months ago
- ☆52Updated 9 months ago
- 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
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆31Updated 3 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆69Updated 5 months ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- Physics informed neural network for learning seepage flow models☆18Updated 2 years ago
- Physics-encoded recurrent convolutional neural network☆46Updated 3 years ago
- POD-PINN code and manuscript☆53Updated 11 months ago
- ☆26Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆36Updated 5 months 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 9 months ago
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆62Updated 3 months ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆84Updated last year
- Towards Physics-informed Deep Learning for Turbulent Flow Prediction☆27Updated 3 years ago
- Encoding physics to learn reaction-diffusion processes☆105Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- Physics Informed Fourier Neural Operator☆23Updated 10 months ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆25Updated 3 months ago
- Physics-informed deep super-resolution of spatiotemporal data☆47Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆83Updated last month
- Deep finite volume method☆22Updated last year
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆156Updated last year