feat(domino): support domino

Author: xiaoyewww

examples/domino/conf/config.yaml

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+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+project: # Project name
+  name: AWS_Dataset
+
+exp_tag: 1 # Experiment tag
+# Main output directory.
+output: outputs/${project.name}/${exp_tag}
+
+hydra: # Hydra config
+  run:
+    dir: ${output}
+  output_subdir: hydra  # Default is .hydra which causes files not being uploaded in W&B.
+
+data: # Input directory for training and validation data
+  input_dir: /lustre/rranade/modulus_dev/data/volume_data/
+  input_dir_val: /lustre/rranade/modulus_dev/data/volume_data_val/
+  bounding_box: # Bounding box dimensions for computational domain
+    min: [-3.5, -2.25 , -0.32]
+    max: [8.5 , 2.25  , 3.00]
+  bounding_box_surface: # Bounding box dimensions for car surface
+    min: [-1.1, -1.2 , -0.32]
+    max: [4.5 , 1.2  , 1.2]
+
+# The directory to search for checkpoints to continue training.
+resume_dir: ${output}/models
+
+variables:
+  surface:
+    solution:
+      # The following is for AWS DrivAer dataset.
+      pMeanTrim: scalar
+      wallShearStressMeanTrim: vector
+  volume:
+    solution:
+      # The following is for AWS DrivAer dataset.
+      UMeanTrim: vector
+      pMeanTrim: scalar
+      nutMeanTrim: scalar
+
+model:
+  model_type: combined # train which model? surface, volume, combined
+  loss_function: "mse" # mse or rmse
+  interp_res: [128, 64, 48] # resolution of latent space
+  use_sdf_in_basis_func: true # SDF in basis function network
+  positional_encoding: false # calculate positional encoding?
+  volume_points_sample: 8192 # Number of points to sample in volume per epoch
+  surface_points_sample: 8192 # Number of points to sample on surface per epoch
+  geom_points_sample: 200_000 # Number of points to sample on STL per epoch
+  surface_neighbors: true # Pre-compute surface neighborhood from input data
+  num_surface_neighbors: 7 # How many neighbors?
+  use_surface_normals: true # Use surface normals and surface areas for surface computation?
+  use_only_normals: true # Use only surface normals and not surface area
+  integral_loss_scaling_factor: 0 # Scale integral loss by this factor
+  normalization: min_max_scaling # or mean_std_scaling
+  encode_parameters: true # encode inlet velocity and air density in the model
+  geometry_rep: # Hyperparameters for geometry representation network
+    base_filters: 16
+    geo_conv:
+      base_neurons: 32 # 256 or 64
+      base_neurons_out: 1
+      radius_short: 0.1
+      radius_long: 0.5 # 1.0, 1.5
+      hops: 1
+    geo_processor:
+      base_filters: 8
+    geo_processor_sdf:
+      base_filters: 8
+  nn_basis_functions: # Hyperparameters for basis function network
+    base_layer: 512
+  aggregation_model: # Hyperparameters for aggregation network
+    base_layer: 512
+  position_encoder: # Hyperparameters for position encoding network
+    base_neurons: 512
+  geometry_local: # Hyperparameters for local geometry extraction
+    neighbors_in_radius: 64
+    radius: 0.05 # 0.2 in expt 7
+    base_layer: 512
+  parameter_model:
+    base_layer: 512
+    scaling_params: [30.0, 1.226] # [inlet_velocity, air_density]
+
+train: # Training configurable parameters
+  epochs: 500
+  checkpoint_interval: 1
+  dataloader:
+    batch_size: 1
+    pin_memory: true
+  sampler:
+    shuffle: true
+    drop_last: false
+  checkpoint_dir: /lustre/rranade/modulus_dev/modulus_forked/modulus/examples/cfd/external_aerodynamics/domino/outputs/AWS_Dataset/3/models/
+
+val: # Validation configurable parameters
+  dataloader:
+    batch_size: 1
+    pin_memory: true
+  sampler:
+    shuffle: true
+    drop_last: false
+
+eval: # Testing configurable parameters
+  test_path: /lustre/rranade/benchmarking/drivaer_aws_surface_test_new/
+  save_path: /lustre/rranade/domino/mesh_predictions_surf_final1/
+  checkpoint_name: DoMINO.0.50.pt
+
+data_processor: # Data processor configurable parameters
+  kind: drivaer_aws # must be either drivesim or drivaer_aws
+  output_dir: /lustre/rranade/modulus_dev/data/volume_data/
+  input_dir: /lustre/datasets/drivaer_aws/drivaer_data_full/
+  num_processors: 12

examples/domino/layers/__init__.py

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+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.