lu-group / deeponet-extrapolationLinks
Reliable extrapolation of deep neural operators informed by physics or sparse observations
☆27Updated 2 years ago
Alternatives and similar repositories for deeponet-extrapolation
Users that are interested in deeponet-extrapolation are comparing it to the libraries listed below
Sorting:
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 years ago
- POD-PINN code and manuscript☆52Updated 9 months ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- ☆54Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- Competitive Physics Informed Networks☆31Updated 11 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- ☆29Updated 2 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆56Updated 7 months ago
- PDE Preserved Neural Network☆54Updated 3 months ago
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated last year
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 7 months ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆81Updated 3 years ago
- ☆42Updated 2 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆29Updated last year
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆72Updated 2 years ago
- ☆112Updated 6 months ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆51Updated last year
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆23Updated 2 years ago
- ☆99Updated 3 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 2 months ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- ☆36Updated 2 years ago
- Applications of PINOs☆132Updated 2 years ago
- Original implementation of fast PINN optimization with RBA weights☆58Updated 4 months ago
- ☆27Updated last year