CNO / config /config.yaml
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# Paper-faithful configuration for arXiv:2302.01178, Table 12 and Appendix C.
experiment:
name: cno_navier_stokes_2d
paper: https://arxiv.org/pdf/2302.01178
seed: 0 # Paper-unspecified, explicit reproducibility choice.
deterministic: true
paths:
data_dir: /public/share/sugonhpcapp01/onestore/onedatasets/RPB_CNO/data
train_file: NavierStokes_64x64_IN.h5
id_test_file: NavierStokes_64x64_IN.h5
ood_test_file: NavierStokes_128x128_OUT.h5
checkpoint: weight/best_model.pth
results_dir: results
data:
input_key: input
output_key: output
train:
start: 0
stop: 750
validation:
start: 768
stop: 896
test_id:
start: 896
stop: 1024
test_ood:
start: 0
stop: 128
# The paper requires [0,1] training normalization and reuse at test time but
# does not publish its constants. These extrema were measured once from the
# supplied 64x64 ID benchmark and are fixed for every split, including OOD.
normalization:
source: supplied_64x64_id_benchmark
input_min: -1.4294605255126953
input_max: 1.4294605255126953
output_min: -2.0383081436157227
output_max: 2.0602376461029053
epsilon: 1.0e-12
model:
in_channels: 1
out_channels: 1
base_width: 32 # d_e in Table 12.
levels: 3 # M in Table 12.
bottleneck_residual_blocks: 8
intermediate_residual_blocks: 1
kernel_size: 3
latent_channels: 64 # Paper-unspecified lift/project internal width.
activation_upsampling_factor: 2 # N_sigma.
filter_taps: 12 # N_tap.
filter_half_width: 0.8 # c_h.
cutoff_denominator: 2.0001
leaky_relu_slope: 0.2 # Paper-unspecified, official supplemental fact.
training:
epochs: 1000
batch_size: 32
num_workers: 4
optimizer: Adam
learning_rate: 0.001
weight_decay: 1.0e-10
scheduler: StepLR
scheduler_step_size: 1
scheduler_gamma: 0.98
early_stopping_patience: 50
log_interval: 5
device: auto
inference:
batch_size: 16
num_workers: 2
device: auto
metric_epsilon: 1.0e-12
paper_reference:
metric: relative_median_l1_percent
id: 2.76
ood: 7.04