Use Global Hook

For properties which are applied at the same value to every node in a model, like global properties, the global hook should be used.

  1. Open the global_inputs folder.
    Inside the training_files folder, there are seven .h3d files named run__000X_m_1_fillet_cantilever.h3d and seven .json files named run__000X_m_1_fillet_cantilever.json. Each .json file contains exactly two material properties, Young’s modulus and Poisson ratio with different values. It is necessary to have consistent labels and number of properties across all the samples.
    Figure 1. Global Property Inputs


  2. Open HyperMesh.
  3. From the menu bar, click View > Ribbons > PhysicsAI to open the PhysicsAI ribbon.
  4. Create a new project called global_inputs.
    1. From the PhysicsAI ribbon, select the Create Project tool.
      Figure 2.


      The Create Project dialog opens.
    2. In the Create Project dialog, enter global_inputs for Project Name.
    3. For Location, click Choose and select a save location for the project.
      Note: The save location for the project contains all files created by PhysicsAI, but the original files used for training do not need to reside in the project folder.
    4. Click OK.
  5. Copy the global_inputs_hook.py file into the _hooks folder inside the project folder.
  6. Create a new dataset using all seven samples and name it Beam.
    1. From the PhysicsAI ribbon, select the Create Dataset tool.
      Figure 3.


      The Create Dataset dialog opens.
    2. For Dataset Name, enter Beam.
    3. For File System, click and navigate to the training_files folder.
      Note: The E and nu labels are selectable inputs to the training.
    4. Select and transfer all of the .h3d files.
      Figure 4.


    5. Click OK.
      The dataset is extracted and the Datasets dialog opens.
    6. Click Close.
  7. Train the model.
    1. From the PhysicsAI ribbon, select the Train an ML Model tool.
      Figure 5.


      The Train Model dialog opens.
    2. For Model Name, enter Displacement.
    3. For Training Data, verify Beam is selected.
    4. For Inputs, select cae.coord, cae.model_data.nu, and cae.model_data.E.
    5. For Outputs, select Displacement.
    6. Click Train.
      Figure 6. Global Property Inputs


      The Model Training dialog opens.
      Tip: Once the status changes to Running, you can click Show Log view the training logs.
  8. Test the model.
    1. From the PhysicsAI ribbon, select the Test ML Model tool.
      Figure 7.


      The Test Model dialog opens.
    2. In the Models area, select Displacement.
    3. In the Datasets area, select Beam and click OK.
      Figure 8.


      The Model Testing dialog opens.
    4. In the Model Testing dialog, select Displacement, and click Display File to view the results in the modeling window.
      Figure 9.


    5. Close the Model Testing dialog.
    New samples can be generated by copying and renaming one or more training files. Ensure that the associated .json files are also present with different values of E and nu while creating the test dataset.
  9. Once the testing is complete, set the model to active.
    Important: Setting a model as active will checkout a stacking license until the model is deactivated.
    1. From the PhysicsAI ribbon, select the Manage ML Models tool.
      Figure 10.


      The Model Training dialog opens.
    2. Click Set Active Model.
Currently, the prediction feature for a model is available through the HyperMesh CFD GUI and in batch mode. The CAD or solver deck to be used for prediction should have an associated .json file named _predict_inp.json which should be saved in the _hooks folder.