NonLinearSystemNeuralNetworkFMU.jl

Generate Neural Networks to replace non-linear systems inside OpenModelica 2.0 FMUs.

Table of Contents

• Training Data Generation
• • Functions
  • Structures
  • Examples
• NonLinearSystemNeuralNetworkFMU.jl
• • Table of Contents
  • Overview
  • Installation
  • Include ONNX into exported FMU
  • Functions
  • Example
• Main Data Generation Routine
• • Functions
  • Example
• Profiling Modelica Models
• • Functions
  • Structures
  • Examples
• Train Machine Learning Surrogate
• • Example

Overview

The package generates an FMU from a modelica file in 3 steps (+ 1 user step):

1. Find non-linear equation systems to replace.

   • Simulate and profile Modelica model with OpenModelica using OMJulia.jl.
   • Find slowest equations below given threshold.
   • Find depending variables specifying input and output for every non-linear equation system.
   • Find min-max ranges for input variables by analyzing the simulation results.

2. Generate training data.

   • Generate 2.0 Model Exchange FMU with OpenModelica.
   • Add C interface to evaluate single non-linear equation system without evaluating anything else.
   • Re-compile FMU.
   • Initialize FMU using FMI.jl.
   • Generate training data for each equation system by calling new interface.

3. Create ONNX (performed by user).

   • Use your favorite environment to create a trained Open Neural Network Exchange (ONNX) model.
     • Use the generated training data to train artificial neural network.

4. Integrate ONNX into FMU.

   • Replace equations with ONNX evaluation done by ONNX Runtime in generated C code.
   • Re-compile FMU.
     • Environment variable ORT_DIR has to be set and point to the ONNX runtime directory (with include/ and lib/ inside).

Installation

See AMIT-HSBI/NonLinearSystemNeuralNetworkFMU.jl README.md for installation instructions.