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FNO Training and Evaluation

This page describes the FNO workflow using the generic training framework.

Entry points and configs

  • Training entry point: src/train.py
  • Evaluation entry point: src/evaluate.py
  • Base config: src/config/default.yaml
  • FNO example config: src/config/fno.yaml

fno.yaml inherits default.yaml and sets FNO-specific model/data/training/eval/output values.

Train an FNO model

From src/:

python train.py --config-name fno

With overrides:

python train.py --config-name fno training.epochs=50 model.params.num_fno_layers=6

Training writes:

  • checkpoint: output.checkpoint (for example ../data/models/lid_driven_fno.mdlus)
  • run metadata: run_meta.json next to the checkpoint

run_meta.json stores resolved data fields, split names, adapter, model entrypoint, and model params.

Evaluate an FNO checkpoint

From src/:

python evaluate.py --config-name fno

You can also pass checkpoint explicitly:

python evaluate.py --config-name fno eval.checkpoint=../data/models/lid_driven_fno.mdlus

Evaluation reads run_meta.json from the checkpoint directory (or eval.run_meta if set), reconstructs the dataset/split, and computes element-weighted metrics.

Optional plots during evaluation

python evaluate.py --config-name fno output.plot_dir=../data/models/lid_driven_fno_plots

Plot controls:

  • output.plot_max_cases
  • output.plot_case_indices
  • output.plot_velocity_x_field
  • output.plot_velocity_y_field
  • output.plot_quiver_step
  • output.plot_cmap
  • output.plot_dpi

Other built-in models

The generic training framework supports multiple model families through the adapter pattern. All use the same train.py / evaluate.py entry points.

Model Config name Adapter Use case
FNO fno grid Regular-grid operator learning
AFNO (custom) grid Adaptive Fourier neural operator
Pix2Pix (custom) grid Image-to-image translation
MeshGraphNet (custom) graph Unstructured mesh GNN
MLP (FullyConnected) alpha_d_mlp pointwise Tabular/axial-profile surrogate

The MLP model uses the pointwise adapter, which reads per-case .zarr stores containing features/ and targets/ arrays (tabular data). See Alpha-D Surrogate Tutorial for the full workflow.

Hyperparameter optimization

All models support Optuna-based hyperparameter optimization via train.py. Add an hpo section to the training config that defines a search space over training.* and model.params.* paths.

cd src && python train.py --config-name alpha_d_mlp

See Hyperparameter Optimization Guide for search-space format, study settings, output artifacts, and how to add HPO for new models.

Notes

  • The legacy wrappers train_fno.py / eval_fno.py are removed.
  • Use train.py / evaluate.py for all supervised one-step models, including FNO.