Jasiuk-Research-Group / S-DeepONetLinks
A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural network in the trunk. The branch network efficiently encodes time-dependent input functions, and the trunk network captures the spatial dependence of the full-field data.
☆18Updated last year
Alternatives and similar repositories for S-DeepONet
Users that are interested in S-DeepONet are comparing it to the libraries listed below
Sorting:
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆36Updated 3 years ago
- POD-PINN code and manuscript☆57Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆37Updated 2 months ago
- ☆54Updated 3 years ago
- MIONet: Learning multiple-input operators via tensor product☆44Updated 3 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Implementation of 'Physics-Informed Neural Networks for Shell Structures' (European Journal of Mechanics A)☆45Updated last year
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆59Updated 4 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 5 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆27Updated last year
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- A method based on a feed forward neural network to solve partial differential equations in nonlinear elasticity at finite strain based on…☆73Updated 8 months ago
- ☆92Updated last year
- Physics-guided neural network framework for elastic plates☆50Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 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
- PDE Preserved Neural Network☆59Updated 8 months ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆28Updated last year
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆37Updated 2 years ago
- Implementation of Physics-Informed Neural Networks for Computational Mechanics based on the DeepXDE package.☆59Updated this week
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆76Updated 2 years ago
- ☆25Updated 5 years ago
- ☆45Updated 3 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆67Updated 3 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…☆57Updated last year