# 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