Operator Models#

class physicsnemo.models.domino.model.DoMINO(
input_features: int,
output_features_vol: int | None = None,
output_features_surf: int | None = None,
global_features: int = 2,
model_parameters=None,
)[source]#

Bases: Module

DoMINO model architecture for predicting both surface and volume quantities.

The DoMINO (Deep Operational Modal Identification and Nonlinear Optimization) model is designed to model both surface and volume physical quantities in aerodynamic simulations. It can operate in three modes: 1. Surface-only: Predicting only surface quantities 2. Volume-only: Predicting only volume quantities 3. Combined: Predicting both surface and volume quantities

The model uses a combination of: - Geometry representation modules - Neural network basis functions - Parameter encoding - Local and global geometry processing - Aggregation models for final prediction

Parameters:

  • input_features (int) – Number of point input features

  • output_features_vol (int, optional) – Number of output features in volume

  • output_features_surf (int, optional) – Number of output features on surface

  • model_parameters – Model parameters controlled by config.yaml

Example

>>> from physicsnemo.models.domino.model import DoMINO
>>> import torch, os
>>> from hydra import compose, initialize
>>> from omegaconf import OmegaConf
>>> device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
>>> cfg = OmegaConf.register_new_resolver("eval", eval)
>>> with initialize(version_base="1.3", config_path="examples/cfd/external_aerodynamics/domino/src/conf"):
...    cfg = compose(config_name="config")
>>> cfg.model.model_type = "combined"
>>> model = DoMINO(
...         input_features=3,
...         output_features_vol=5,
...         output_features_surf=4,
...         model_parameters=cfg.model
...     ).to(device)

Warp … >>> bsize = 1 >>> nx, ny, nz = cfg.model.interp_res >>> num_neigh = 7 >>> global_features = 2 >>> pos_normals_closest_vol = torch.randn(bsize, 100, 3).to(device) >>> pos_normals_com_vol = torch.randn(bsize, 100, 3).to(device) >>> pos_normals_com_surface = torch.randn(bsize, 100, 3).to(device) >>> geom_centers = torch.randn(bsize, 100, 3).to(device) >>> grid = torch.randn(bsize, nx, ny, nz, 3).to(device) >>> surf_grid = torch.randn(bsize, nx, ny, nz, 3).to(device) >>> sdf_grid = torch.randn(bsize, nx, ny, nz).to(device) >>> sdf_surf_grid = torch.randn(bsize, nx, ny, nz).to(device) >>> sdf_nodes = torch.randn(bsize, 100, 1).to(device) >>> surface_coordinates = torch.randn(bsize, 100, 3).to(device) >>> surface_neighbors = torch.randn(bsize, 100, num_neigh, 3).to(device) >>> surface_normals = torch.randn(bsize, 100, 3).to(device) >>> surface_neighbors_normals = torch.randn(bsize, 100, num_neigh, 3).to(device) >>> surface_sizes = torch.rand(bsize, 100).to(device) + 1e-6 # Note this needs to be > 0.0 >>> surface_neighbors_areas = torch.rand(bsize, 100, num_neigh).to(device) + 1e-6 >>> volume_coordinates = torch.randn(bsize, 100, 3).to(device) >>> vol_grid_max_min = torch.randn(bsize, 2, 3).to(device) >>> surf_grid_max_min = torch.randn(bsize, 2, 3).to(device) >>> global_params_values = torch.randn(bsize, global_features, 1).to(device) >>> global_params_reference = torch.randn(bsize, global_features, 1).to(device) >>> input_dict = { … “pos_volume_closest”: pos_normals_closest_vol, … “pos_volume_center_of_mass”: pos_normals_com_vol, … “pos_surface_center_of_mass”: pos_normals_com_surface, … “geometry_coordinates”: geom_centers, … “grid”: grid, … “surf_grid”: surf_grid, … “sdf_grid”: sdf_grid, … “sdf_surf_grid”: sdf_surf_grid, … “sdf_nodes”: sdf_nodes, … “surface_mesh_centers”: surface_coordinates, … “surface_mesh_neighbors”: surface_neighbors, … “surface_normals”: surface_normals, … “surface_neighbors_normals”: surface_neighbors_normals, … “surface_areas”: surface_sizes, … “surface_neighbors_areas”: surface_neighbors_areas, … “volume_mesh_centers”: volume_coordinates, … “volume_min_max”: vol_grid_max_min, … “surface_min_max”: surf_grid_max_min, … “global_params_reference”: global_params_values, … “global_params_values”: global_params_reference, … } >>> output = model(input_dict) >>> print(f”{output[0].shape}, {output[1].shape}”) torch.Size([1, 100, 5]), torch.Size([1, 100, 4])

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