SMArtInt+

Our Modelica Library for simple and user-friendly Integration of neuronal networks in Modelica models

 

There is an increasing need for hybrid models consisting of physical and surrogate as well as data-driven models. XRG has developed an interface, the SMArtInt+ Library, that enables the easy integration of neural networks from different sources and types. SMArtInt stands for Smart Integration of Artificial Intelligence in Modelica. The reduced open source version SMArtInt, which is already available in the Dymola Library Portfolio, has been successfully tested in many real applications and is available free of charge on github. 

There are numerous AI frameworks for creating neural networks: e.g. TensorFlow, PyTorch, Flux.jl (Julia) and others. The SMArtInt+ library enables you to integrate machine learning models from different sources in a standardized, user-friendly and efficient way via an external source that can be changed independently of the Modelica code. Currently, SMArtInt+ supports TensorFlow and ONNX models, which can be exported to Dymola and OpenModelica or imported from Modelica blocks.

Example model for integration of Stateful Recurrent Neural Networks

These typical types of neural networks are supported:

  • Quasistatic Feed Forward Neural Networks (FFNN)
  • Dynamic Recurrent Neural Networks (RNN), stateful or non-stateful
  • Neural Ordinary Differential Equations (NODE)
  • Generative models, working with Encoder/Decoder
  • Complex applications like BNODE

Comparison of SMArtInt+ vs. SMArtInt

The commercial version contains the following valuable features compared to the free version:

  • Extrapolation warning (animated and as a prompt warning) if the inputs leave the convex hull of the training data or the inputs are in an area with low training data density 
  • Additional I/O features: scaling and smoothing 
  • Automatic Modelica block generation (from Python or Modelica) 
  • Python converter for sRNN from TensorFlow to TensorFlow lite 
  • Dedimensionalization of neural networks to make them independent of absolute inputs 
  • and of course our XRG product support

Use Case

In the DIZPROVI research project, a surrogate model of a steam heat exchanger was created using measurement data. The results of the hybrid data-driven model (blue line and dots) are shown in the following graphics in comparison to the purely physical model (red line and dots). A scenario was compared using measurement data that was not part of the training data set.

Compatibility

The library has been tested and is available for the current versions of:

  • Dymola
  • OpenModelica