bedrock-checkpoints-58

bedrock-checkpoints-58

Physics-informed neural-network (PINN) surrogate trained on STAR-CCM+ RANS CFD results for steady coolant flow. The model maps geometry-aware coordinate features to flow fields and uses three geodesic flow-connectivity features in addition to the prior 55-feature local-chart representation.

The metadata below was extracted from starccm_pinn_model.pt and cross-checked against the included training configuration and loss history.

Checkpoint: starccm_pinn_model.pt

The checkpoint is the final artifact from the included loss history, whose last row is step 600,000. It contains model and pressure-head weights, Adam optimizer state, scheduler state, normalization arrays, feature/output contracts, STAR-CCM+ metadata, and geometry-similarity metadata.

Outputs (7)

Public output order:

u, v, w, p, k, epsilon, temperature

The network internally predicts p_shape; physical pressure is reconstructed from the outlet reference and the geometry-conditioned pressure-drop head:

p = outlet_pressure + delta_p * p_shape

Supervised fields are u, v, w, and pressure-derived targets. k, epsilon, and temperature are latent physics-constrained fields.

Architecture

All 58 inputs are Fourier-encoded with 8 frequency bands (sine and cosine), producing 986 encoded values before the MLP.

The separate pressure-drop head predicts log(delta_p) from 11 geometry descriptors using a ridge-linear branch plus a nonlinear correction branch:

geometry_regressor: 11 -> 1
geometry_mlp:       11 -> 32 -> 32 -> 1

STAR-CCM+ physics

Input feature names (58)

 0. x
 1. y
 2. z
 3. distance_to_inlet
 4. distance_to_outlet
 5. distance_to_wall
 6. axial_inlet_to_outlet
 7. radial_to_inlet_outlet_axis
 8. signed_distance_proxy
 9. wall_proximity
10. source_x_norm
11. source_y_norm
12. source_z_norm
13. source_axis_axial
14. source_axis_lateral_1
15. source_axis_lateral_2
16. source_axis_radial
17. chart_center_x_norm
18. chart_center_y_norm
19. chart_center_z_norm
20. chart_center_axis_axial
21. chart_center_axis_lateral_1
22. chart_center_axis_lateral_2
23. chart_local_x
24. chart_local_y
25. chart_local_z
26. chart_local_axis_axial
27. chart_local_axis_lateral_1
28. chart_local_axis_lateral_2
29. chart_radius
30. chart_log_count
31. chart_wall_distance_mean
32. chart_wall_distance_std
33. chart_cov_eig_1
34. chart_cov_eig_2
35. chart_cov_eig_3
36. chart_anisotropy
37. chart_planarity
38. wall_distance_x_minus
39. wall_distance_x_plus
40. wall_distance_y_minus
41. wall_distance_y_plus
42. wall_distance_z_minus
43. wall_distance_z_plus
44. surface_area
45. fluid_volume
46. surface_area_to_volume_ratio
47. equivalent_hydraulic_diameter
48. surface_genus
49. cross_section_area_min
50. cross_section_area_mean
51. cross_section_area_std
52. cross_section_area_min_ratio
53. number_of_strong_constrictions
54. high_curvature_surface_fraction
55. geodesic_distance_from_inlet
56. geodesic_distance_to_outlet
57. flow_coordinate_s

Loading

This is a full training checkpoint rather than a standalone serialized model class. Use the included trainer architecture to instantiate the network, then load the state dictionaries.

import torch

ckpt = torch.load(
    "starccm_pinn_model.pt",
    map_location="cpu",
    weights_only=False,
)

model_state = ckpt["model"]
pressure_head_state = ckpt["pressure_head"]

feature_names = ckpt["feature_names"]       # 58 inputs
output_order = ckpt["output_order"]         # public physical outputs
pressure_output_order = ckpt["pressure_output_order"]

# Input standardization
feature_mean = ckpt["feature_mean"]
feature_scale = ckpt["feature_scale"]

# Supervised-label standardization (u, v, w, p)
label_mean = ckpt["label_mean"]
label_scale = ckpt["label_scale"]

Only load pickle-based PyTorch checkpoints from sources you trust. The weights_only=False setting is required here because the checkpoint contains NumPy metadata in addition to tensors.

Included files

• starccm_pinn_model.pt: final checkpoint at step 600,000
• starccm_pinn_loss_history.csv: 3,001 logged records through step 600,000
• bedrock_trainer_dim55.py: training/evaluation implementation (the filename is historical; this run uses 58 inputs)
• build_local_chart_starccm_enriched_data_cuda.py: CUDA data builder for enriched local-chart features
• bedrock_dim58_dp_head_geodesic.yaml: run configuration