Add the Datacenter use case

CHANGELOG.md

@@ -14,6 +14,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added matrix decomposition scheme to improve graph partitioning
 - DrivAerML dataset support in FIGConvNet example.
 - Retraining recipe for DoMINO from a pretrained model checkpoint
+- Added Datacenter CFD use case.
 
 ### Changed
 
@@ -23,6 +24,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Moved non-dimensionaliztion out of domino datapipe to datapipe in domino example
 - Updated utils in `modulus.launch.logging` to avoid unnecessary `wandb` and `mlflow` imports
 - Moved to experiment-based Hydra config in Lagrangian-MGN example
+- Make data caching optional in `MeshDatapipe`
 
 ### Deprecated
 

examples/cfd/datacenter/README.md

@@ -0,0 +1,84 @@
+# Thermal and airflow surrogate model for Datacenter design
+
+This example demonstrates the use of a Deep Learning model (3D UNet) for training a
+surrogate model for datacenter airflow to enable real-time datacenter design.
+The aim of this workflow is to train a Deep Learning model that can predict the
+temperature and airflow distribution within a hot aisle of a typical datacenter.
+For any given geometry of the hot aisle (height, width, and length) and the number
+of IT racks inside it, the trained model can predict the temperature, velocity,
+and pressure distribution inside the hot aisle instantaneously. Such an approach
+can be very useful from a datacenter design perspective where iterating through
+various design combinations is crucial to obtain optimal cooling and minimize
+hotspots.
+
+![Design study using the AI surrogate model](../../../docs/img/datacenter_design_cfd.gif)
+
+## Dataset
+
+The model is trained on OpenFOAM simulation data. Based on the variables, i.e.,
+Length, Height, Width, and Number of Racks, several hot aisle configurations are
+generated. These configurations are solved with OpenFOAM assuming maximum flow
+rate and rack exit temperature (max load condition). Steady state simulations
+are used and the resulting OpenFOAM data is exported in VTK format for training
+of the AI surrogate. The dataset is then normalized using the mean and standard
+deviation statistics of the dataset. The normalized dataset, along with a sample
+OpenFOAM configuration, can be downloaded using the NGC link: [Link to be added].
+
+**Note:** Access to NVAIE is required to download the dataset
+and the reference OpenFOAM configuration.
+
+**Note:** The OpenFOAM configuration provided is only representative.
+Several key aspects have been masked to protect the IP.
+Users should not expect to generate the training data
+exactly using this setup, and one will have to change
+it using their own geometries and boundary conditions.
+
+## Training
+
+A UNet model is used in this problem. The hex-dominant mesh used in this problem
+makes this model an attractive choice offering good speed and accuracy. Since
+the model is primarily trained to capture the changes in geometry, we use the
+Signed Distance Field of the interior of the hot aisle to capture the parameter
+variation. Additionally, we add sinusoidal embeddings to enable the model to
+capture sharp features in the flow field. Finally, to make the different
+datacenter sizes uniform (for ingestion into UNet), we pad the geometry for the
+maximum hot aisle dimensions. This padding is removed before computing the loss.
+
+The model can be trained by executing the below commands:
+
+```bash
+python train.py
+```
+
+To train on multiple GPUs,
+
+```bash
+mpirun -np <#GPUs> python train.py
+```
+
+Once the model is trained, you can use the inference.py script to compute the
+model inference. For generating the Signed Distance Field and geometry for the
+inference, we make use of the utilities from Modulus-Sym.
+
+### Training of Physics-Informed model
+
+We also train a variant where we add the physics losses to the data loss.
+The physics-informed training can be executed using the below commands:
+
+```bash
+python train_physics_informed.py
+```
+
+Addition of such physics data losses proves very beneficial in the low-data
+regime where the physics losses can compensate for the lack of enough data.
+
+![Comparison of data+physics driven training with pure data driven training](../../../docs/img/datacenter_hybrid_training.png)
+
+## Contributors
+
+This example was developed as a part of collaboration between NVIDIA and Wistron.
+
+## Resources
+
+1. [Wistron Uses NVIDIA Omniverse and NVIDIA Modulus to Build Digital Twin Platform, Transforming Factory Planning and Operations](https://www.wistron.com/en/Newsroom/2024-03-19-1)
+2. [Model Innovators: How Digital Twins Are Making Industries More Efficient](https://blogs.nvidia.com/blog/digital-twins-modulus-wistron/)

examples/cfd/datacenter/conf/config.yaml

@@ -0,0 +1,33 @@
+# 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.
+
+hydra:
+  job:
+    chdir: True
+  run:
+    dir: ./outputs
+
+start_epoch: 1
+max_epochs: 260
+
+start_lr: 1e-3
+lr_scheduler_gamma: 0.99975
+
+train_batch_size: 2
+val_batch_size: 2
+
+train_num_samples: 768
+val_num_samples: 192

examples/cfd/datacenter/conf/config_inference.yaml

@@ -0,0 +1,21 @@
+# 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.
+
+hydra:
+  job:
+    chdir: True
+  run:
+    dir: ./outputs

examples/cfd/datacenter/conf/config_physics_informed.yaml

@@ -0,0 +1,35 @@
+# 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.
+
+hydra:
+  job:
+    chdir: True
+  run:
+    dir: ./outputs
+
+start_epoch: 1
+max_epochs: 260
+
+start_lr: 1e-3
+lr_scheduler_gamma: 0.99975
+
+phy_wt: 0.001
+
+train_batch_size: 2
+val_batch_size: 2
+
+train_num_samples: 64
+val_num_samples: 192