katiana22 / TL-DeepONetLinks
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
☆75Updated 2 years ago
Alternatives and similar repositories for TL-DeepONet
Users that are interested in TL-DeepONet are comparing it to the libraries listed below
Sorting:
- ☆54Updated 3 years ago
- MIONet: Learning multiple-input operators via tensor product☆43Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆107Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 4 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- ☆110Updated 4 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆43Updated 2 years ago
- POD-PINN code and manuscript☆57Updated last year
- ☆30Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 4 months ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆59Updated 3 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆76Updated 2 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆75Updated 8 months ago
- A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural ne…☆18Updated last year
- ☆117Updated 11 months ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆54Updated 2 years ago
- Competitive Physics Informed Networks☆32Updated last year
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 2 months ago
- Original implementation of fast PINN optimization with RBA weights☆68Updated 4 months ago
- ☆165Updated 3 years ago
- Implementation of PINNs in TensorFlow 2☆81Updated last month
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆97Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- PDE Preserved Neural Network☆59Updated 8 months ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆147Updated 4 years ago