# PointCFD main experiment from arXiv:2010.09469.
experiment:
  name: pointcfd_main
  paper: https://arxiv.org/pdf/2010.09469

paths:
  data_dir: /public/share/sugonhpcapp01/onestore/onedatasets/PointNetCFD_data
  data_file: CFDdata.npy
  train_indices: training_idx.npy
  validation_indices: validation_idx.npy
  test_indices: test_idx.npy
  checkpoint: weight/best_model.pth
  results_dir: results

data:
  num_points: 1024
  source_channels: [x, y, p, u, v]
  input_indices: [0, 1]
  target_indices: [3, 4, 2]
  input_names: [x, y]
  target_names: [u, v, p]
  coordinate_normalization: none
  target_normalization: train_minmax

model:
  input_dim: 2
  output_dim: 3
  global_feature_dim: 1024
  expected_paper_parameters: 3552588

training:
  # The paper does not report a random seed; zero is the reproducible default.
  seed: 0
  epochs: 4000
  batch_size: 256
  num_workers: 0
  optimizer: adam
  learning_rate: 0.0005
  beta1: 0.9
  beta2: 0.999
  epsilon: 0.000001
  weight_decay: 0.0
  scheduler: none
  precision: float32
  validation_interval: 1
  log_every_batches: 1
  # The paper validates each epoch but does not define checkpoint selection.
  best_metric: val_mse

evaluation:
  relative_l2_epsilon: 1.0e-12
  visualization_cases: 3
  paper_reference_mean_relative_l2:
    u: 0.0449666
    v: 0.0370540
    p: 0.0271661