| # FuXi-CFD Model |
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| ## Overview |
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| This repository accompanies the paper: |
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| **Reconstructing fine-scale 3D wind fields with terrain-informed machine learning** |
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| It provides the pre-trained FuXi-CFD model used in the study, exported in ONNX format, together with a complete inference example. |
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| **Version:** v1.0 |
| **Framework:** ONNX (runtime inference) |
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| --- |
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| ## Model Description |
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| FuXi-CFD is a terrain-informed deep learning model designed to reconstruct fine-scale three-dimensional wind fields from coarse atmospheric inputs and high-resolution terrain information. |
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| ### Model Inputs |
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| The model expects an `inputs.npz` file containing: |
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| - `dem` — terrain elevation (300 × 300, float32, meters) |
| - `roughness` — surface roughness length (300 × 300, float32, meters) |
| - `u_100m` — coarse zonal wind at 100 m (9 × 9, float32, m s⁻¹) |
| - `v_100m` — coarse meridional wind at 100 m (9 × 9, float32, m s⁻¹) |
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| All variables must be provided in physical units (no normalization applied by the user). |
| Normalization parameters used during training are included in `normalization/`. |
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| ### Model Outputs |
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| The model produces a file `prediction.npz` containing: |
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| - `u` — zonal wind component (27, 300, 300), m s⁻¹ |
| - `v` — meridional wind component (27, 300, 300), m s⁻¹ |
| - `w` — vertical wind component (27, 300, 300), m s⁻¹ |
| - `k` — turbulent kinetic energy (27, 300, 300), m² s⁻² |
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| The vertical levels correspond to the 27 non-uniform heights described in the associated dataset documentation. |
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| --- |
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| ## Quick Start (Inference Example) |
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| ```bash |
| cd inference_example |
| python scripts/infer.py \ |
| --model ../model/fuxicfd_model.onnx \ |
| --input data/inputs.npz \ |
| --output data/prediction.npz |
| ``` |
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| Outputs are saved as `data/prediction.npz` with keys: `u, v, w, k`. |
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| --- |
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| ## Repository Structure |
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| - `model/` — exported ONNX weights |
| - `inference_example/` — complete preprocessing → inference → postprocessing pipeline |
| - `normalization/` — training-time normalization parameters |
| - `scripts/` — runnable inference scripts |
| - `utils/` — helper functions |
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| --- |
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| ## License |
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| CC BY-NC 4.0 |
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| --- |
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| ## Citation |
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| If you use this model, please cite: |
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| Lin, C., et al. *Reconstructing fine-scale 3D wind fields with terrain-informed machine learning*, Nature Communications (2026). |
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