bedrock-checkpoints

Physics-informed neural-network (PINN) surrogate trained on STAR-CCM+ RANS CFD results (coolant flow). The model maps geometry/coordinate features to the steady flow fields. Metadata below is auto-extracted from the checkpoint header.

Checkpoint: starccm_pinn_model_step_00390000.pt

Training step	390000
Feature schema	canonical_local_chart_dim55_geometry_conditioned_v1
Input features (semantic)	55
Encoded input dim (Fourier)	935 = 55 raw + 55×2×8 freqs
Output variables	7
Hidden width	384
Linear layers	7
Main network params	1,101,319
Pressure-head params	1,485
Total params	1,102,804

Outputs (7)

Order: u, v, w, p, k, epsilon, temperature Supervised labels: n/a (remaining fields are latent / physics-constrained).

Architecture (main field MLP)

Inputs are Fourier-encoded (8 frequencies, sin+cos) before the MLP.

layer	shape (in → out)
mlp.net.0.weight	935 → 384
mlp.net.2.weight	384 → 384
mlp.net.4.weight	384 → 384
mlp.net.6.weight	384 → 384
mlp.net.8.weight	384 → 384
mlp.net.10.weight	384 → 384
mlp.net.12.weight	384 → 7

A separate pressure head regresses a scalar pressure-drop (ΔP) per geometry from 11 geometry descriptors (geometry_mlp: 11 → 32 → 32 → 1).

STAR-CCM+ physics

setting	value
turbulence_model	RkeTwoLayerTurbModel
wall_treatment	KeTwoLayerAllYplusWallTreatment
inlet_temperature_k	298.15
inlet_mass_flow_lpm	25.0
starccm_version	2402.0001 / 19.02.013

Input feature names (55)

 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

Loading

import torch
ckpt = torch.load("starccm_pinn_model_step_00390000.pt", map_location="cpu", weights_only=False)
model_state = ckpt["model"]          # field-network weights
feature_names = ckpt["feature_names"]  # 55 inputs
output_order = ckpt["output_order"]    # 7 outputs
# input standardization: ckpt["feature_mean"], ckpt["feature_scale"]
# label  standardization: ckpt["label_mean"],   ckpt["label_scale"]

Card generated from hosseinbv/bedrock-checkpoints checkpoint metadata.