UNO (U-shaped Neural Operator) combines the multiscale encoder–decoder structure of U-Net with the spectral convolutions of a Fourier Neural Operator. It learns PDE solution operators across multiple spatial scales while preserving local flow-field details through skip connections.
This model package targets Navier–Stokes time-series prediction on two-dimensional regular grids. By default, it takes the first 10 time steps as input and predicts the subsequent 10 autoregressively.
This repository is a minimal, self-contained, runnable UNO model package maintained by OneScience for ModelScope downloads, automated OneCode execution, and rapid local validation.
Supported capabilities:
Train a two-dimensional UNO model from a YAML configuration
Perform multistep autoregressive prediction of Navier–Stokes flow fields
Compute relative L2 errors and save predicted tensors and visualizations
Override the sample count, temporal window, spatial downsampling, and model size from the command line
Run on a CPU, GPU, or DCU
Unsupported capabilities:
Pretrained weights are not bundled
The approximately 394 MiB raw Navier–Stokes data file is not bundled
The standalone model includes only the two-dimensional UNO implementation required by this example; the general-purpose OneScience 1D and 3D components are not included
Use Cases
Use Case
Description
Flow-field time-series prediction
Autoregressively predict future Navier–Stokes states from historical vorticity fields
Neural operator training
Evaluate the combination of Fourier spectral convolutions and a U-shaped multiscale architecture
CFD surrogate modeling
Learn mappings from historical to future fields on regular grids
Pipeline validation
Validate training and inference with a small sample set and a single epoch
File Structure
Path
Purpose
Notes
README.md
Project documentation
English
conf/config.yaml
Data, model, training, and output configuration
Paths are resolved relative to the model package root
model/uno.py
Standalone two-dimensional UNO model
Does not depend on onescience.modules
scripts/common.py
Shared configuration, device, metric, and autoregressive utilities
Used by both training and inference
scripts/train.py
Training and validation script
Saves the checkpoint with the best relative L2 error
scripts/inference.py
Inference, evaluation, and visualization script
Loads weight/*.pt
data/
Navier–Stokes data directory
Stores the benchmark .mat file
weight/
Model weight directory
Checkpoints are written automatically during training
result/
Inference result directory
Created automatically on first inference
Usage
1. OneCode
Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience:
The OneScience community also provides the training data. Download it with the following command and verify that the data path in conf/config.yaml is configured correctly:
The default checkpoint is saved to weight/uno_navier_stokes.pt. The training script is controlled entirely by conf/config.yaml and does not accept command-line configuration arguments.
Model Weights
This repository provides weights trained on the standard Navier–Stokes dataset in the weight/ directory.
Inference, Evaluation, and Visualization
python scripts/inference.py
The script loads weight/uno_navier_stokes.pt by default and generates the following files under result/:
The inference script is likewise controlled entirely by conf/config.yaml and does not accept command-line configuration arguments. The weight path is determined jointly by training.weight_dir and training.checkpoint_name; the output directory and number of saved samples are controlled by inference.result_dir and inference.num_samples, respectively.
Configuration
conf/config.yaml contains five sections:
common: device and random seed
datapipe: data file, sample splits, temporal windows, downsampling, and DataLoader settings
model: hidden channels, Fourier modes, normalization, and spatial padding
training: optimizer, learning-rate schedule, early stopping, and weight directory
inference: inference result directory and number of saved samples
The model automatically updates in_dim from t_in * out_dim in the configuration, so the model input dimension does not need to be synchronized manually when the history window changes.
Data Format
The .mat file must contain a variable named u with the following standard shape:
[1200, 64, 64, 20]
Dimension
Meaning
1200
Number of independent flow-field samples
64, 64
Height and width of the two-dimensional regular grid
