This package provides several blocks which can be used to include different types of neural networks within Modelica models. These blocks can be included in models, but their in- and outputs are still generic arrays. The user has to fill the arrays in the same manner as they are used during training in python.

Steps to include a model:

  1. Create and train a model in TensorFlow
  2. Export a trained TensorFlow model as TfLite model
  3. Place the corresponding block in your model
  4. Parametrize the block (provide path, number of in- and outputs, etc.)
  5. Connect the in- and outputs of the block. The arrays have the same structure as those in TensorFlow: the inputs have to be connected in the same manner as they are used in the neural network during training.

The examples PipeLocalHeatTransfer and TF_PI_Stateful use this approach.



Generated at 2024-07-20T18:16:01Z by OpenModelicaOpenModelica 1.23.1 using GenerateDoc.mos