Conference Agenda

We stabilized the general Magnetic Marker Monitoring approach to obtain a more robust, reliable online reconstruction of a single magnetic dipole source form acquired multichannel magnetic field measurements with millisecond temporal resolution. By expansion of our smart approach the presence of multiple magnetic dipole sources can efficiently be analysed.

We aim to establish an optimization pipeline for individualized electrode positioning in repetitive
transorbital alternating current stimulation (rtACS) for patients with vision loss. The pipeline
includes individual MRI data, from which a finite element (FE) head model is built. Recorded
visual fields are used to define the goal function for the optimization. We adapt an optimization
technology which was established for transcranial electrical stimulation (TES) using multiple
targets for our transorbital application. In contrast to most TES applications our goal is to
maximize the current density within the target regions, without considering direction and focality
of the field. In proof of principle with a single subject and 29 different combinations of targets,
we achieved an average current density in the target regions of 0.18 ± 0.02 A/m2 with the
individual montages, compared 0.09 ± 0.01 A/m2 in the standard electrode montage. We
therefore established a pipeline for individualized electrode positioning for rtACS.

We analyse and quantify the amount of heat generated by a nanoparticle, injected in a background medium, while excited by incident electromagnetic waves. These nanoparticles are dispersive with electric permittivity following the Lorentz model. The purpose is to determine the quantity of heat generated extremely close to the nanoparticle (at a distance proportional to the radius of the nanoparticle). This study extends our previous results, derived in the 2D TM and TE regimes, to the full Maxwell system. We show that by exciting the medium with incident frequencies close to the Plasmonic or Dielectric resonant frequencies, we can generate any desired amount of heat close to the injected nanoparticle while the amount of heat decreases away from it. These results offer a wide range of potential applications in the areas of photo-thermal therapy, drug delivery, and material science, to cite a few.
To do so, we employ time-domain integral equations and asymptotic analysis techniques to study the corresponding mathematical model for heat generation. This model is given by the heat equation where the body source term comes from the modulus of the electric field generated by the used incident electromagnetic field. Therefore, we first analyse the dominant term of this electric field by studying the full Maxwell scattering problem in the presence of Plasmonic or All-dielectric nanoparticles. As a second step, we analyse the propagation of this dominant electric field in the estimation of the heat potential. For both the electromagnetic and parabolic models, the presence of the nanoparticles is translated into the appearance of large scales in the contrasts for the heat-conductivity (for the parabolic model) and the permittivity (for the full Maxwell system) between the nanoparticle and its surrounding.

A beamformer application for real-time source reconstruction was implemented for the open-source framework MNE-CPP. The performance of the new beamformer application was investigated regarding computation speed for simulated magnetoencephalography (MEG) data. Preliminary results show that a single source is reconstructed at the expected location and within a time sufficient for real-time applications.

Studying changes in cortical sources and functional connectivity in subjects with epilepsy can offer valuable insights into the alterations caused by the disease. To investigate these changes, a customized pipeline is used to compute functional connectivity based on EEG signals measured on the scalp. The inverse problem solution, which is a computational approach used to estimate the related cortical source areas, is proposed to identify detectable pre-ictal functional activities at source levels. This approach allows to compare the alterations occurring in both the electrodes and cortical sources during the pre-ictal state of the brain in subjects with epilepsy. Graph measures based on Phase Lag Index (PLI) are employed to quantify phase differences between signals originating from distinct brain areas, while considering their corresponding connection strengths, integration and segregation. This analysis assesses the level of connectivity and information exchange among different brain regions, investigating mechanisms and potential markers associated with epileptic activity. Preliminary results of the connectivity analysis will be presented, along with the necessity of determining cortical sources through the estimation of the inverse problem for accurate identification of the anomaly.

Physics informed neural networks (PINNs) are at the center of attention in diversity of applications, especially for material data identification and engineering. In this work, we investigate the feasibility of material data identification using PINNs. We aim to identify thermo-physical properties, such as specific heat, thermal conductivity, as well as B-H characteristics relevant to hysteresis of materials. We show that the thermo-physical properties are identified quite accurately using few temperature sensor data of an air cooled cylindrical sample. Moreover, the B-H characteristics of the pure iron is approximated applying PINNs by incorporating the physics of the equivalent magnetic circuit of the yoke-based measurement setup and using a secondary voltage and primary excitation signal of the transformer as an input.

A temperature driven three-dimensional velocity field of a liquid metal in a Rayleigh-Bénard cell is reconstructed employing contactless inductive flow tomography. The procedure has been enhanced to account for the electrical conductivity of the heat exchanger that stimulates the flow. The impact of the design of the heat exchanger onto the reconstructed velocity field is analysed.

This article presents an innovative approach to diagnose the state of a corroded ship's hull by utilizing measurements of the electric field in its vicinity.

To be done!!

This work suggests an efficient method based on anisotropic polynomial chaos ex-
pansions for performing sensitivity analysis for multivariate model outputs. Generalised variance
based (Sobol) sensitivity indices are used to quantify the sensitivity of the multivariate output to
the model inputs. The suggested method is applied to an electric machine model which features
vector-valued quantities of interest, e.g., the torque-speed characteristic. Comparisons against
sensitivity analyses based on Monte Carlo sampling and isotropic polynomial chaos expansions
reveal the significant accuracy and efficiency gains of the proposed method.

In this work, a Deep Learning approach based on a Conditional Variational Autoencoder (CVAE) has been adopted for the solution of an inverse problems of magnetic field reconstruction knowing the field on a subdomain. Subsequently, starting from the CVAE outputs, the geometry of the field source can be identified. Two different techniques are used: a deep artificial neural network, fully connected, and a convolutional neural network. The proposed methods are applied to the TEAM 35 benchmark magnetostatic problem and a comparison between them is done.