Towards Integration of PeN-ODEs in a Modelica-based workflow

Abstract

Hybrid modeling – the combination of first-principle modelsand machine learning – offers the potential to increasemodel accuracy while reducing modeling effort. Althoughapproaches for creating hybrid models from systemsimulation models exist, the unique characteristics ofModelica-based, object-oriented models – such as modularityand reusability – can, as of today, not be utilized. Inthis contribution, we explore approaches for bridging thisgap to enable the use of hybrid models with Modelica. Keychallenges of architecture definition, training environmentand reintegration of the trained machine learning partsinto a Modelica model are addressed. To illustrate ourapproach, we present a case study involving a SCARA robot.This example demonstrates a partially integrated workflowfor hybrid modeling, intended to serve as a foundation andmotivation for further research.