docs: complete English reproduction card

README.md

@@ -10,17 +10,19 @@ library_name: pytorch
 
 # RONOM
 
-## Model
+## Model and experiment
 
-This package reproduces the one-dimensional viscous Burgers input generalization experiment. The trained checkpoint is `weight/best_model.pth`; model code, configuration, training, inference, and visualization scripts are included. The package was trained once; reported numbers are not guarantees for another run.
+This package reproduces the one-dimensional viscous Burgers input generalization experiment from the [paper](https://arxiv.org/abs/2507.12814). RONOM combines a convolutional reduced-order encoder, a latent Neural ODE, and a decoder. This experiment uses 33 spatial points, 11 time points, four latent channels, and fixed-step RK4 integration.
+
+The package includes model code, `config/config.yaml`, training, inference and visualization scripts, the recorded `results/`, and the selected `weight/best_model.pth` checkpoint.
 
 ## Data
 
 Download [OneScience/RONOM-Burgers](https://www.modelscope.cn/datasets/OneScience/RONOM-Burgers) with `modelscope download --dataset OneScience/RONOM-Burgers --local_dir ./data`. Burgers.mat contains 1,000 trajectories: input [1000,1025], output [1000,101,1025], tspan [1,101], float64. Split: 600 train, 200 validation, 200 test.
 
-Data provenance: [https://zenodo.org/records/21451819](https://zenodo.org/records/21451819).
+Original dataset: https://zenodo.org/records/21451819 Download the dataset before running inference where an external dataset is required. The release configuration uses relative paths, not SCNet personal-directory paths.
 
-## Usage
+## Installation and usage
 
 ```bash
 hf download OneScience-Group/RONOM --local-dir ./RONOM
@@ -30,8 +32,16 @@ python scripts/inference.py
 python scripts/result.py
 ```
 
-For training, run `python scripts/train.py` after installing the appropriate accelerator-enabled PyTorch environment and downloading the data above. See `config/config.yaml` for experiment parameters. Preserve the included checkpoint before retraining.
+For a fresh training run, use an allocated GPU/DCU compute node with a compatible PyTorch build, keep a separate copy of the supplied checkpoint, then run `python scripts/train.py` from the package root. The full budget is specified in `config/config.yaml`. Cluster-specific Slurm scripts, if present, need adaptation outside the original cluster.
+
+## Training and reported result
+
+The run completed the configured 5,000 epochs. The first 600 trajectories were used for training, the next 200 for validation, and the final 200 for an independent test.
+
+Mean test-trajectory RMSE was 0.00170540 versus 0.00134 in paper Table 1(a), about 27.3% higher. Mean relative L2 was 0.00986709. See `results/` for the recorded metrics and visualizations. The supplied checkpoint comes from a completed run; another run may differ.
 
-## Result and citation
+## Source and license
 
-test trajectory RMSE: 0.00170540; paper Table 1(a): 0.00134. See `results/` for the recorded outputs and the [original paper or data page](https://zenodo.org/records/21451819) for the research context. The dataset license, where not separately stated by its author, must not be inferred from the code license.
+- Paper: https://arxiv.org/abs/2507.12814.
+- Dataset source: https://zenodo.org/records/21451819.
+- This model package uses the license indicated in the card metadata. Dataset redistribution terms, where not separately stated, must not be inferred from the code license.