Paving the way for Hybrid Twins using Neural Functional Mock-Up Units

Abstract

Porting Neural Ordinary Differential Equations (NeuralODEs), the combination of an artificial neural network and an ODE solver, to real engineering applications is still a challenging venture. However, we will show that Neural Functional Mock-up Units (NeuralFMUs), an evolved subgroup of NeuralODEs that contain Functional Mock-up Units (FMUs), are able to cope with these challenges. This paper briefly introduces to the topics NeuralODE and NeuralFMU and describes the procedure and considerations to apply this technique to a real engineering use case. Further, different workflows to apply NeuralFMUs dependent on tool capabilities and use case requirements are discussed. The presented method is illustrated with the creation of a Hybrid Twin of an hydraulic excavator arm, which has various challenges such as discontinuity, nonlinearity, oscillations and characteristic maps. Finally we will show, that the created Hybrid Twin, on basis of measurement data from a real system, gives more accurate results compared to a conventional simulation model based on first principles.