kopanicakova / HINTS_precondLinks
This repository contains code, which was used to generate large-scale results in the HINTS paper.
☆33Updated last year
Alternatives and similar repositories for HINTS_precond
Users that are interested in HINTS_precond are comparing it to the libraries listed below
Sorting:
- ☆28Updated 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…☆56Updated 10 months ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last week
- ☆47Updated 8 months ago
- Code for Mesh Transformer describes in the EAGLE dataset☆42Updated 9 months ago
- ☆37Updated 4 months ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- ☆32Updated 3 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆26Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated last year
- A novel DeepONet architecture that is specifically designed for generating predictions on different 3D geometries discretized by differen…☆22Updated last year
- ☆54Updated 3 years ago
- The MegaFlow2D dataset package☆23Updated 2 years ago
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆63Updated 4 months ago
- ☆45Updated 2 years ago
- ☆29Updated 3 years ago
- An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.☆20Updated last month
- MIONet: Learning multiple-input operators via tensor product☆39Updated 3 years ago
- PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations☆24Updated 5 months ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆74Updated 2 years ago
- [ICLR 2024] Neural Spectral Methods: Self-supervised learning in the spectral domain.☆47Updated last year
- Fourier Neural Operators to solve for Allen Cahn PDE equations☆19Updated 3 years ago
- Code repository for "Learned Turbulence Modelling with Differentiable Fluid Solvers"☆39Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆19Updated last year
- [NeurIPS 2025] Geometry Aware Operator Transformer As An Efficient And Accurate Neural Surrogate For PDEs On Arbitrary Domains☆63Updated 3 weeks ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆17Updated last year
- ☆44Updated 3 months ago