This repository contains code accompaning the paper: U-NO: U-shaped Neural Operators
UNO_Tutorial.ipynb - A step by step tutorial for using and buidling U-NO. Link to Google colab
pytorch 1.11.0
| Files | Descriptions |
|---|---|
| integral_operators.py | Contains codes for Non-linear integral operators for 1D, 2D, and 3D functions. |
| UNO_Tutorial.ipynb | A tutorial on uisng the integral operators and U-NO. |
| Darcy Flow | |
| darcy_flow_main.py | Script for loading data,training and evaluating training UNO performing 2D spatial covolution for solving Darcy Flow equation. |
| darcy_flow_uno2d.py | UNO achitectures for solving Darcy Flow equation. |
| train_darcy.py | Training routine for Darcy flow equations. |
| data_load_darcy.py | Function to load Darct-flow data. |
| Navier-Stocks | |
| data_load_navier_stocks.py | Function to load Navier-Stocks data generated by data generator prodived |
| ns_uno2d_main.py | Script for loading data,training and evaluating the UNO (2D) autogressive in time for Navier-Stocks equation. |
| ns_train_2d.py | Training function for UNO(2D) in time for Navier-Stocks equation |
| navier_stokes_uno2d.py | UNO(2D) achitectures in time for Navier-Stocks equation. |
| ns_uno3d_main.py | Script for loading data,training and evaluating the UNO(3D) performing 3D (spatio-temporal) convolution for Navier-Stocks equation. |
| navier_stokes_uno3d.py | UNO(3D) achitectures performing 3D convolution for Navier-Stocks equation. |
| ns_train_3d.py | Training function for UNO(3D) for Navier-Stocks equation. |
| Supporting Files | |
| Data Generation | Folder contains scripts to generate data from Navier-stocks equation and Darcy flow |
| utilities3.py | Contains supporting functions for data loading and error estimation. |
Link to two files containing 2000 simulations of Darcy Flow equation: Google Drive Link
The Data Generator folder contains script for generating simulation of Darcy Flow and Navier-Stocks equation.