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:
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆56Updated 9 months ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last month
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- ☆33Updated 3 months ago
- Code for Mesh Transformer describes in the EAGLE dataset☆42Updated 8 months ago
- Discontinuity Computing Using Physics-Informed Neural Network☆26Updated last year
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- ☆32Updated 3 years ago
- ☆46Updated 7 months ago
- ☆28Updated last year
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆29Updated 2 years ago
- The MegaFlow2D dataset package☆23Updated 2 years ago
- ☆43Updated 2 months ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated last year
- ☆55Updated 3 years ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆17Updated last year
- MIONet: Learning multiple-input operators via tensor product☆38Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- A novel DeepONet architecture that is specifically designed for generating predictions on different 3D geometries discretized by differen…☆21Updated last year
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆62Updated 4 months ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆74Updated 2 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- Code repository for "Learned Turbulence Modelling with Differentiable Fluid Solvers"☆39Updated 3 years ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 3 years ago
- ☆29Updated 3 years ago
- Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 20…☆14Updated 7 years ago
- [NeurIPS 2025] Geometry Aware Operator Transformer As An Efficient And Accurate Neural Surrogate For PDEs On Arbitrary Domains☆45Updated last week
- PINNs for 2D Incompressible Navier-Stokes Equation☆54Updated last year
- An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.☆20Updated 2 weeks ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago