The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

Independent of the respective NFC (near field communication) application the specific NFC device has to be tested for standard compliance. In the present work we propose a hybrid surrogate-model based synthesis strategy which enables the incorporation of standard compliance tests in the synthesis process. While the surrogate model is used in early optimization phases applying a stochastic optimization strategy, the highfidelity model is applied in the final deterministic optimization part. The high fidelity model is based on a thin wire partial element equivalent circuit method.

This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified on synthetic as well as measurement data.

Understanding physical effects occurring, for example, in the electromagnetic field, can be challenging. To ease the learning experience, it is beneficial to visualize and encourage interaction with the physical field. Augmented Reality (AR) can serve as a tool to visualize naturally invisible fields to help students understand physical effects. In this paper, we present a workflow to incorporate field results stemming from a FEM tool or simple analytical solutions into an augmented reality (AR) experience. We focus on providing a simple framework for educators to integrate this tool into school or university teaching. We present a workflow to process simulation results for AR and provide source material through a template and a guide so that educators can quickly translate their projects into (augmented) reality. The basis for this project is the game engine Unity, which can be used free of charge for educators. Combined with other free or open-source programs for visualization and preparation like openCFS and Paraview, this setup can be used freely by anybody for education.

The aim of this work is to optimize the design of a sensor-actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before the identification problem, i.e. finding the material parameters causing the measurement data, is solved, the design of the measurement setup should first be improved in order to increase the identifiability of the material distribution. For the objective function of the design optimization the Fisher information matrix (FIM) is used, which allows to quantity the amount of information that the measurements carry about the unknown parameters. To evaluate the magnetic field values depending on various design parameters and material distributions, a 2D magneto-static problem is solved by the finite element method. Due to the high numerical effort arising with evaluating the FIM and thus calculating the forward model many times within the optimization procedure, a surrogate model of the sensor-actuator system has been trained in advance and is used to predict the magnetic flux densities.

In this work, we present an approach for training parametrized physics informed neural networks to solve PDEs in a self supervised fashion, which means that no labeled input-output data is needed to train the neural network. The main contribution of this work is the achievement of a model with parameterizable boundary condition functions. This means that no retraining is needed to produce correct results for changing boundary conditions.

Abstract Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical radar unit is replaced by a virtual radar model. Driving datasets, such as the nuScenes dataset, containing large amounts of annotated sensor measurements, help understand sensor capabilities and play an important role in sensor modeling. This article includes a thorough analysis of the radar data available in the nuScenes dataset. Radar properties, such as detection thresholds, and detection probabilities depending on object, environment, and radar parameters, as well as object properties, such as reflection behavior depending on object type, are investigated quantitatively. The overall detection probability of the considered radar (Continental ARS-408-21) was found to be 27.81%. Four radar models on object level with different complexity levels and different parametrisation requirements are presented: a simple RCS-based radar model with an accuracy of 51%, a linear SVC model with an accuracy of 70%, a Random Forest model with an accuracy of 83%, and a Gradient Boost model with an accuracy of 86%. The feature importance analysis of the machine learning algorithms revealed that object class, object size, and object visibility are the most important parameters for the presented radar models. In contrast, daytime and weather conditions seem to have only minor influence on the modeling results.