.SMArtInt.BaseClasses

Information

Base Classes

The package containes templates to include different types of neural networks in Modelica. One find blocks within the model which have to be connected with the dessired in- and outputs. The user should extend the model, give all parameters and give a interface which has to be connected to the inner blocks.

Steps to include a model:

1. Create and train a model in, e.g., TensorFlow
2. Export a trained model as TfLite or ONNX model
3. Extend the appropriate base class
4. Parametrize the model (provide path, number of in- and outputs, etc.)
5. Define the interface and connect the in- and outputs of the blocks. The arrays have the same structure as those in the AI model (e.g. TensorFlow model): the inputs have to be connected in the same manner as they are used in the neural network during training.

The examples PipeLocalHeatTransfer_smallNN uses this approach to create a model. The BaseClasses are also extended by the various neural network blocks.

Contents

Name	Description
BaseNeuralNet
BaseGenericNeuralNet
BaseFeedForwardNeuralNet
BaseRecurrentNeuralNet
BaseStatefulRecurrentNeuralNet