This example demonstrates how to set up a purely physics-driven model for solving a Lid
Driven Cavity (LDC) flow using PINNs. The goal of this example is to demonstrate the
interoperability of Modulus, Modulus-Sym and PyTorch. This example adopts a workflow
where appropriate utilities are imported from modulus, modulus.sym and torch to
define the training pipeline.
Specifically, this example demonstrates how the geometry and physics utilites from Modulus-Sym can be used in custom training pipelines to handle geometry objects (typically found in Computer Aided Engineering (CAE)) workflows and introduce physics residual and boundary condition losses.
This example takes a non-abstracted way to define the problem. The boundary condition constraints, residual constraints, and the subsequent physics loss computation are defined explicitly. For a more abstracted version of this workflow, where some of these steps are automated and abstracted, we recommend the Introductory example tutorial from Modulus-Sym.
If you are running this example outside of the Modulus container, install Modulus Sym using the instructions from here
To train the model, run
python train.pyThis should start training the model. Since this is training in a purely Physics based fashion, there is no dataset required.
Instead, we generate the geometry using the Modulus Sym's geometry module and sample
point cloud using GeometryDatapipe utility. For more details refer documentation
here
For computing the physics losses, we will use the PhysicsInformer utility from
Modulus-Sym. For more details, refer documentation
here
The results would get saved in the ./outputs/ directory.
This example demonstrates computing physics losses on point clouds. For more examples on physics informing different type of models and model outputs, refer:
- Point clouds: Darcy Flow (DeepONet), Stokes Flow (MLP)
- Regular grid: Darcy Flow (FNO)
- Unstructured meshes: Stokes Flow (MeshGraphNet)