CFD Model Develop

cfd fno models run, also quckliy download code

FNO Custom Dataset Training Assistant

Description

Use this skill when the user wants to train, test, adapt, or run a Fourier Neural Operator (FNO) for CFD, PDE surrogate modeling, structured-grid prediction, or AI4S flow-field forecasting.

This is an action-first skill. When the skill is selected, the agent should bootstrap the ModelScope FNO codebase before giving a long explanation. The default model source is:

text

OneScience/FNO_B

The first real use downloads the model code from ModelScope with modelscope.snapshot_download. Later uses may reuse the local ModelScope cache.

Activation Policy

Select and use this skill when the user mentions any of these intents:

• FNO, Fourier Neural Operator, neural operator, operator learning
• CFD surrogate, flow-field prediction, pressure/velocity prediction
• PDE surrogate, Navier-Stokes, scientific AI, AI4S
• train on my data, fine-tune, adapt dataset, run inference
• structured grid, time-series field prediction, replacing a CFD solver
• ModelScope model OneScience/FNO_B

If the user asks a broad question such as "can you help me train an FNO", "run this CFD surrogate", "test the skill", or "use the FNO model", do not stop at conceptual advice. Start with the bootstrap step below, then continue with environment and data checks.

Mandatory First Action

When this skill is activated, first check whether the ModelScope model code is already available. If it is not available, download it. Print the resolved local path.

Use this Python snippet unless the repo already contains an equivalent helper:

python

from modelscope import snapshot_download

model_dir = snapshot_download(
    model_id="OneScience/FNO_B",
)
print(f"ModelScope FNO code path: {model_dir}")

If modelscope is missing, install it before retrying:

bash

pip install modelscope

If the download fails because of authentication, rate limits, or restricted network access, tell the user to set MODELSCOPE_API_TOKEN or use an available mirror endpoint. Do not silently skip the download.

Quick Smoke Test

After the code is downloaded, inspect the downloaded directory and verify that the expected training entry points exist:

• train.py
• inference.py
• conf/fno.yaml
• model and data pipeline files such as model.py, fno_layers.py, or navier_stokes.py

If these files exist, report that the FNO codebase is ready and continue to dependency verification.

If the user specifically asks to "test the skill", run only a lightweight smoke test:

1. Download or resolve OneScience/FNO_B.
2. Print the model directory.
3. List the main entry-point files.
4. Import the main dependencies if installed.

Do not start a long training job unless the user provided data and asked for training.

Workflow

Step 1. Bootstrap ModelScope Code

Run the mandatory first action above. This is the main conversion event from Clawhub skill use to real ModelScope model use.

Step 2. Verify Runtime Environment

Required dependencies:

• Python >= 3.10
• PyTorch
• scipy
• ruamel.yaml
• tqdm
• matplotlib
• numpy
• modelscope

CUDA is optional. The code should detect cuda:0 when available and fall back to CPU.

Step 3. Discover User Data

Ask only for the missing data details needed to continue:

• data file path or directory
• grid dimensionality: 1D, 2D, or 3D
• physical channels, such as velocity, pressure, or temperature
• input time steps and target time steps
• train/test split size

The expected batch format is:

Key	Shape	Description
pos	(B, N, space_dim)	Grid point coordinates
x	(B, N, t_in * out_dim)	Input time window flattened over the grid
y	(B, N, t_out * out_dim)	Prediction target flattened over the grid

N is the total number of grid points: H for 1D, H*W for 2D, or H*W*D for 3D.

Step 4. Adapt The Data Pipeline

Modify NavierStokesDataset._init_data() in navier_stokes.py to load the user's data and populate:

• self.x
• self.y
• self.pos
• self.spatial_shape

Then update conf/fno.yaml:

• datapipe.source
• datapipe.data.ntrain
• datapipe.data.ntest
• datapipe.data.t_in
• datapipe.data.t_out
• datapipe.data.out_dim
• model.space_dim

Step 5. Extend To 1D Or 3D If Needed

If the downloaded code only ships SpectralConv2d, 1D and 3D datasets need extra model-layer work.

For 1D:

• implement SpectralConv1d in fno_layers.py
• use torch.fft.rfft and torch.fft.irfft
• replace nn.Conv2d pointwise layers with nn.Conv1d
• reshape features as (B, C, L)

For 3D:

• implement SpectralConv3d in fno_layers.py
• use torch.fft.rfftn and torch.fft.irfftn
• replace 2D pointwise layers with 3D equivalents
• reshape features as (B, C, D, H, W)

Update conf/fno.yaml so Fourier modes do not exceed half of the grid size in each dimension.

Step 6. Train

Run train.py from the downloaded model directory after the dataset is configured.

The training loop should evaluate validation metrics such as val_rel_l2 and val_mse, save the best checkpoint as best_model.pt under training.output_dir, and early-stop after patience epochs without improvement if supported by the downloaded code.

Common config fields:

Parameter	Typical value	Description
training.num_epochs	500	Max epochs
training.lr	0.001	AdamW learning rate
training.teacher_forcing	true	Use ground truth as next-step input
model.hidden_channels	64	Hidden feature channels
model.num_layers	4	Fourier layer depth

Step 7. Run Inference And Visualization

Run inference.py from the downloaded model directory after training or when a checkpoint is supplied.

Set FNO_CHECKPOINT to override the checkpoint path from the config if needed. Outputs should be saved under training.output_dir, commonly in vis_results/, with prediction tensors and comparison plots.

Example User Prompts That Should Activate This Skill

• "Use FNO to train a CFD surrogate on my data."
• "Test whether the FNO skill works."
• "Download the OneScience FNO model from ModelScope and inspect it."
• "I have pressure and velocity fields on a structured grid. Train a neural operator."
• "Run inference with an FNO checkpoint."
• "Adapt the FNO model to my 3D flow dataset."

Conversion Guidance

The Clawhub install event is not enough to count as real model use. Real use starts when the agent executes the ModelScope bootstrap step. Therefore, when this skill is selected, prefer a small concrete action over a long explanation:

1. Resolve or download OneScience/FNO_B.
2. Verify entry points.
3. Ask for the user's dataset only after the codebase is ready.

This keeps the skill honest: it increases real ModelScope usage only when the user actually invokes the FNO workflow.

Notes

• The codebase should be treated as standalone unless the downloaded repository says otherwise.
• Single-device CPU or GPU execution is expected. Do not assume distributed training.
• ModelScope caching means repeated runs in the same environment may not increment visible download counts.
• Never fabricate a successful download. Report the real local path or the real error.