.SMArtIInt.Blocks

Information

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.

Contents

NameDescription
 EvaluateGenericNeuralNetwork
 EvaluateSimpleFeedForwardNeuralNetwork
 EvaluateRecurrentNeuralNet
 EvaluateStatefulRecurrentNeuralNet

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