experiment:
  name: HO-FNO-Pipe-Flow
  seed: 2026
  paper: "Higher-Order Fourier Neural Operator: Explicit Mode Mixer for Nonlinear PDEs"
  task: "Pipe horizontal fluid velocity prediction"

paths:
  data_dir: data
  output_dir: .
  checkpoint: weight/best_model.pth
  results_dir: results

data:
  x_file: Pipe_X.npy
  y_file: Pipe_Y.npy
  q_file: Pipe_Q.npy
  train_size: 1000
  test_size: 200
  total_used: 1200
  input_channels: 2
  output_channel_index: 0
  resolution: [129, 129]
  downsample: [1, 1]
  normalizer_axes: [0, 1]
  pin_memory: true
  num_workers: 2

model:
  class_name: HOFNOPipe
  order: 2
  layers: 8
  width: 128
  modes: [50, 24]
  input_channels: 2
  output_channels: 1
  add_unit_grid: true
  mlp_expansion: 4
  mlp_dropout: 0.0
  drop_path_rate: 0.0

training:
  epochs: 500
  batch_size: 8
  optimizer: AdamW
  learning_rate: 1.0e-3
  final_learning_rate: 5.0e-5
  weight_decay: 1.0e-5
  scheduler: CosineAnnealingLR
  loss: relative_l2
  save_best_metric: test_mean_relative_l2
  print_every: 1

runtime:
  device: auto
  require_accelerator: true
  accelerator_kind: dcu
  slurm:
    partition: hx1hdnormal01
    nodes: 1
    gres: dcu:1
    cpus_per_task: 8
    memory: 30G
    time_limit: "24:00:00"
    module: compiler/dtk/25.04
    conda_activate: /public/software/sghpc_sdk/Linux_x86_64/25.6/das/conda/bin/activate
    conda_env: onescience