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:
- Create and train a model in TensorFlow
- Export a trained TensorFlow model as TfLite model
- Place the corresponding block in your model
- Parametrize the block (provide path, number of in- and outputs, etc.)
- 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.
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