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README.md

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+---
+library_name: phase
+license: mit
+pipeline_tag: image-to-image
+tags:
+- magnetohydrodynamics
+- neural-operator
+- diffusion
+- physics-informed-machine-learning
+---
+
+# PHASE Kelvin-Helmholtz MR
+
+Multi-regime PHASE model for the 2-D incompressible MHD Kelvin-Helmholtz instability on t=[0,5], with gated Re/Rm adapters and residual diffusion.
+
+- **Channels:** [ux, uy, Bx, By]
+- **Reynolds numbers:** Re=Rm in {80, 200, 400, 650, 1000, 1500, 2050, 2750, 3600, 4500}
+- **Conditioner:** `conditioner/model.safetensors`
+- **Diffusion model:** `diffusion/model.safetensors`
+- **Normalization:** `normalization/`
+
+## POSEIDON dependency
+
+```bash
+git clone https://github.com/camlab-ethz/poseidon.git external/poseidon
+git -C external/poseidon checkout b8fa28f59bd7f7673323f28d11a12c6f3a215c61
+pip install --no-deps -e external/poseidon
+```
+
+## Load
+
+```python
+import torch
+from phase import PHASEPipeline
+
+model = PHASEPipeline.from_pretrained(
+    "phaseMHD/PHASE-KH-MR",
+    device="cuda" if torch.cuda.is_available() else "cpu",
+)
+
+# Physical initial condition: [batch, channels, x, y]
+# The KH model was trained on 101 frames from t=0 to t=5.
+times = torch.linspace(0.0, 5.0, 101, device=model.device)
+prediction = model.predict(initial_fields, times, re=1000, seed=0)
+# prediction: [batch, channels, time, x, y]
+```
+
+Install the runtime from [PHASE](https://github.com/PHASE-MHD/PHASE):
+
+```bash
+pip install "phase[hub,dino,scot] @ git+https://github.com/PHASE-MHD/PHASE.git"
+```
+
+The package contains model-only weights, exact inference configs, and training-derived normalization statistics. See `pipeline.yaml` and `provenance.json` for the artifact mapping.

conditioner/config.yaml

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+####################
+## Canonical four-channel gated-adapter multi-Re KH scOT, t=[0,5].
+## Train configs/kh/single_re/scot_re1000.yaml first.
+
+model_params:
+  model_type: "poseidon-mhd-re-finetune"
+  poseidon_model: "camlab-ethz/Poseidon-T"
+  load_pretrained_poseidon: true
+  image_size: 128
+  patch_size: 4
+  out_channels: 4
+  poseidon_input_channel_map: [1, 2]
+  poseidon_output_channel_map: [1, 2]
+  magnetic_channel_indices: [4, 5]
+  fallback_poseidon_num_channels: 6
+  fallback_poseidon_num_out_channels: 6
+  use_poseidon_fluid_normalization: true
+  magnetic_input_init: "mean_velocity"
+  magnetic_output_init: "mean_velocity"
+  velocity_residual: false
+  magnetic_residual: true
+  magnetic_residual_scale: 1.0
+  helmholtz_projection: true
+  project_velocity: true
+  project_magnetic: true
+  helmholtz_domain_size_x: 1.0
+  helmholtz_domain_size_y: 1.0
+  re_conditioning:
+    enabled: true
+    type: "deep_adapter_output_film"
+    hidden_dim: 128
+    num_layers: 2
+    log_re_mean: 2.9756
+    log_re_std: 0.5417
+    include_rem: true
+    residual_scale: 1.0
+    reference_re: 1000.0
+    deep_adapter:
+      target: "all"
+      bottleneck_dim: 64
+      hidden_dim: 128
+      num_layers: 2
+      adapter_scale: 1.0
+      gate_type: "channel"
+
+normalization_params:
+  type: "physics"
+  input_norm: [1.0, 1.0, 8.06614549e-02, 8.06614549e-02]
+  output_norm: [1.0, 1.0, 8.06614549e-02, 8.06614549e-02]
+  paired_input_groups: [[0, 1], [2, 3]]
+  paired_output_groups: [[0, 1], [2, 3]]
+  paired_mode: "rms"
+
+dataset_params:
+  dataset_type: "multi_re"
+  data_root: "${DATA_ROOT}"
+  data_file: "mhd_data_4channel.npy"
+  n: 1000
+  re_values: [80, 200, 400, 650, 1000, 1500, 2050, 2750, 3600, 4500]
+  rem_values: [80, 200, 400, 650, 1000, 1500, 2050, 2750, 3600, 4500]
+  train_size_per_re: 800
+  val_plus_test_size_per_re: 200
+  seed: 42
+  sub_t: 5
+  sub_x: 1
+  t_range: [0.0, 5.0]
+  x_range: [0.0, 1.0]
+  y_range: [0.0, 1.0]
+  balanced_re_batches: true
+  res_per_batch: 10
+
+loss_params:
+  type: "physics-informed-bfield"
+  nu: 0.001
+  eta: 0.001
+  rho0: 1.0
+  data_weight: 10.0
+  ic_weight: 1.0
+  pde_weight: 0.001
+  constraint_weight: 0.0
+  vorticity_weight: 2.0
+  current_weight: 5.0
+  use_data_loss: true
+  use_ic_loss: true
+  use_pde_loss: true
+  use_constraint_loss: false
+  u_weight: 1.0
+  v_weight: 1.0
+  Bx_weight: 5.0
+  By_weight: 5.0
+  u_time_loss_mode: "global"
+  u_time_loss_eps: 1.0e-6
+  v_time_loss_mode: "global"
+  v_time_loss_eps: 1.0e-6
+  magnetic_time_loss_mode: "global"
+  magnetic_time_loss_eps: 1.0e-6
+  derived_time_loss_mode: "global"
+  derived_time_loss_eps: 1.0e-6
+  vorticity_time_loss_mode: "global"
+  vorticity_time_loss_eps: 1.0e-6
+  current_time_loss_mode: "global"
+  current_time_loss_eps: 1.0e-6
+  Du_weight: 1.0
+  Dv_weight: 1.0
+  DBx_weight: 100.0
+  DBy_weight: 100.0
+  div_vel_weight: 0.0
+  div_B_weight: 0.0
+  Lx: 1.0
+  Ly: 1.0
+  tend: 5.0
+  use_weighted_mean: false
+
+dataloader_params:
+  train: {batch_size: 1, num_workers: 4, shuffle: true, pin_memory: true}
+  validation: {batch_size: 1, num_workers: 4, shuffle: false, pin_memory: false}
+  test: {batch_size: 1, num_workers: 4, shuffle: false, pin_memory: false}
+
+optimizer_params:
+  optimizer_type: "adamw"
+  lr: 1.0e-5
+  weight_decay: 1.0e-2
+  betas: [0.9, 0.999]
+  param_groups:
+    enabled: true
+    pretrained_lr: 1.0e-7
+    new_lr: 1.0e-3
+    # Retained for checkpoint/config compatibility. With boundary_group=pretrained,
+    # expanded boundary tensors use pretrained_lr and this value is not active.
+    magnetic_output_lr: 2.0e-3
+    boundary_group: "pretrained"
+    freeze_pretrained: false
+    pretrained_weight_decay: 1.0e-2
+    new_weight_decay: 0.0
+  use_scheduler: false
+  scheduler_type: "dummy"
+
+train_params:
+  recipe: "kh_four_channel_multi_re"
+  epochs: 100
+  checkpoint_path: "${OUTPUT_ROOT}/checkpoints/kh_multi_re_scot_t0_5.pt"
+  warm_start_checkpoint: "${OUTPUT_ROOT}/checkpoints/kh_single_re_scot_re1000.pt"
+  load_checkpoint: ""
+  is_finetune: false
+  checkpoint_metric: "denorm_rel_l2"
+  validation_interval: 5
+  tiny_validation_interval: 1
+  tiny_validation_samples_per_re: 5
+  tiny_validation_batch_size: 10
+  tiny_validation_seed: 42

conditioner/model.safetensors

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diffusion/config.yaml

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+# Canonical ten-regime KH residual diffusion, t=[0,5].
+# Train the single-Re KH residual recipe first; its model weights are the warm start.
+config_type: diffusion
+
+model_params:
+  model_type: elucidated
+  base_dim: 128
+  dim_mults: [1, 2, 3, 5, 8, 12]
+  channels: 4
+  self_condition: true
+  learned_variance: false
+  learned_sinusoidal_cond: false
+  random_fourier_features: false
+  sinusoidal_pos_emb_theta: 10000
+  learned_sinusoidal_dim: 16
+  dropout: 0.0
+  attn_heads: 8
+  attn_dim_head: 64
+  flash_attn: true
+  image_size: 128
+  num_sample_steps: 32
+  sigma_min: 0.002
+  sigma_max: 80
+  sigma_data: 0.5
+  rho: 7
+  P_mean: -1.2
+  P_std: 1.2
+  S_churn: 80
+  S_tmin: 0.05
+  S_tmax: 50
+  S_noise: 1.003
+  field_representation: magnetic
+  helmholtz_projection: true
+  project_velocity: true
+  project_B: true
+  projection_mode: full_field_residual
+  domain_size_x: 1.0
+  domain_size_y: 1.0
+
+normalization_params:
+  type: per_re_paired_minmax
+  channel_groups: [[0, 1], [2, 3]]
+  feature_range: [-1, 1]
+  re_values: [80, 200, 400, 650, 1000, 1500, 2050, 2750, 3600, 4500]
+  default_re: 1000.0
+  inputs_stats_template: ${MODEL_ROOT}/normalization/Re{re}_inputs_stats.npz
+  targets_stats_template: ${MODEL_ROOT}/normalization/Re{re}_residual_targets_stats.npz
+
+dataset_params:
+  train_path: ${FEATURE_ROOT}/kh/multi_re/phase_t0_5/train
+  val_path: ${FEATURE_ROOT}/kh/multi_re/phase_t0_5/val
+  test_path: ${FEATURE_ROOT}/kh/multi_re/phase_t0_5/test
+  seed: 42
+  return_metadata: true
+  balanced_re_batches: true
+  res_per_batch: 10
+  prediction_mode: residual
+  residual_target: true
+  source_time_range: [0.0, 5.0]
+  source_output_dt: 0.02
+  source_sub_t: 5
+  frames_per_trajectory: 51
+
+dataloader_params:
+  train: {batch_size: 64, num_workers: 8, shuffle: true, pin_memory: true}
+train_loader_params:
+  batch_size: 64
+  shuffle: true
+  num_workers: 8
+  pin_memory: true
+
+optimizer_params:
+  optimizer_type: adamw
+  lr: 5.0e-5
+  weight_decay: 1.0e-4
+  betas: [0.9, 0.999]
+  param_groups: {enabled: false}
+  use_scheduler: true
+  scheduler_type: cosine
+  T_max: 1000
+  eta_min: 2.5e-5
+
+train_params:
+  recipe: kh_phase_residual_multi_re
+  epochs: 100
+  checkpoint_path: ${OUTPUT_ROOT}/checkpoints/kh_multi_re_phase_t0_5.pt
+  warm_start_checkpoint: ${OUTPUT_ROOT}/checkpoints/kh_single_re_phase_re1000.pt
+  warm_start_mode: model_weights_only
+  validation_interval: 5
+  checkpoint_metric: denorm_rel_l2
+  clip_grad: true
+  clip_grad_max_norm: 1.0

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pipeline.yaml

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+format_version: 1
+model_family: PHASE
+physical_problem: kelvin_helmholtz
+channels: [ux, uy, Bx, By]
+re_values: [80, 200, 400, 650, 1000, 1500, 2050, 2750, 3600, 4500]
+time_range: [0.0, 5.0]
+prediction_mode: residual
+conditioner:
+  type: scot_gated_adapter_multi_re
+  config: conditioner/config.yaml
+  weights: conditioner/model.safetensors
+  official_tfno_mapping: false
+diffusion:
+  type: edm
+  config: diffusion/config.yaml
+  weights: diffusion/model.safetensors

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