The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.

In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied.

Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of $$0.108\,^{\circ }$$0.108 ∘ C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas the classical calibration method takes more than 1 week.

With the growing demand for autonomous mi-cromobility vehicles, developing robust and effective simulators for them becomes increasingly important. This research paper examines the essential requirements of a simulator for autonomous micromobility vehicles, focusing on aspects such as accurate sensor modeling, realistic pedestrian behavior, customizability, scenario and vehicle library, scalability, and user-friendliness. By analyzing these key features, we provide a comprehensive understanding of the necessary components for an effective simulation environment, aiming to enable researchers, developers, and other stakeholders to design, test, and evaluate autonomous micromobility vehicles in a safe and controlled manner. Addressing these requirements, simulators can significantly contribute to the advancement of autonomous micromobility technology, leading to safer and more efficient urban transportation systems in the future.

Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP.