Conference Agenda

Metamaterials are engineered materials with properties beyond what we encounter in nature. They thus allow unique and previously unattainable interactions between waves and matter. The talk will present current research in this area. It will focus on applications to contemporary (5G) and next-generation (6G and beyond) wireless communication systems.
Current wireless systems already use millimetre waves and will probably also use sub-Terahertz waves by the decade's end. These new, high-frequency bands will open exciting venues to develop novel wireless transmission techniques and scenarios. The talk will discuss physical insights into various electromagnetic metamaterial classes, e.g., all-dielectric effective media and resonating meta-atom structures. It will aim at explaining how they can manipulate electromagnetic waves. The presented application examples will include artificial magnetic conductors, antennas, and intelligent reflecting surfaces.
Modern antenna design benefits significantly from the intense development of new optimisation/improvement approaches and algorithms. Therefore, the talk will also explore various metamaterial optimisation strategies and computational electromagnetic modelling methods inherited from the design of conventional microwave devices and systems, e.g. antennas and body area networks. Finally, the presentation will highlight challenges and emerging open questions specific to the synthesis of metamaterials.

The digest presents a free-shape optimization method which simultaneously takes
into account magnetic and mechanical targets. It is suitable to optimize devices such as syn-
chronous reluctance machines simulated in a finite element context. The process is presented
and illustrated on the optimization of a concrete synchronous reluctance machine.

The paper describes an optimization method to design the compensation networks of a wireless power transfer system considering an electrified line with more inductor buried on the road. The Finite Element Analysis is used to compute mutual and self-inductance whereas a genetic optimization algorithm is used to improve the system efficiency and transmitted power in a car moving conditions.

The paper describes a modelling and solving methodology of a {static converter – electric motor – control} system for its sizing by optimisation, considering the dynamic thermal heating of the machine. A co-simulation is set up for the steady state electrical system aspects and the transient thermal aspects. The simulators are coupled through a master-slave relationship with adapted time step management. A sizing by optimisation example of this system is carried out with a different optimisation algorithm according to the design specification.

Optimizing electrical systems represented by ODE and events, using their frequency spectrum is an important issue for designers. This paper presents a methodology to answer to this issue. Using gradient-based optimization algorithm, the paper proposes to simulate the electrical system according time, and then to compute its frequency spectrum. To optimize it by SQP, Automatic Differentiation is mainly used to compute the model gradients.

Magnetic gears can be considered as possible power transmission systems, in substitution of classical mechanical transmissions. They are able to transmit torque, between two mechanical axes, in a contactless way, through the interaction of two coaxial permanent magnets rotors with a set of ferromagnetic poles. The performance of magnetic gears depends on several geometric and material parameters and their sizing can be approached by optimization.
In this work a multi-objective optimization procedure is used to compute the minimum volume encumbrance to meet a value of the transmitted torque. The optimization is based on a constrained single objective approach and carried out by a deterministic optimization strategy. The analysis of the magnetic structure is performed by two dimensional magnetostatic nonlinear magnetic field analysis that is used to evaluate the maximum value of transmitted torque. Optimization loop is managed by a deterministic constrained technique working on the thickness values of the active and passive parts of the two rotor structures.
Results obtained allow to size the device, finding the minimal radial and axial encumbrance of the gear needed to obtain a given value of transmitted torque.

We present a design method for a 3D printed induction heating coil that is optimized by parameterizing the coil path and cross-section. The objective functions of the optimization are the temperature rises in the heated workpiece in the angular and longitudinal directions. The coil path optimization ensures homogeneous heating in the angular direction, while the cross-section optimization enables local heating in the longitudinal direction. We combine the optimized parameters and verify that the final shape can be printed with 3D printers.

We propose an innovative technique for dealing with optimal shape design problems characterised by curved boundaries between ferromagnetic and dielectric sub-regions. The proposed approach relies on the ability of the Virtual Element Method in handling meshes with polygonal elements having curved edges. The well-known TEAM 25 benchmark problem is considered as case study.

Grid reinforcement is coming. Meanwhile, it is essential to manage the power grid optimally to ensure good power quality. Intelligent power grid management by efficiently distributing grid capacity among customer assets in a low voltage distribution grid, taking into account intermittent distributed energy resources and emerging loads, could be an approach.

Power System Optimization Models are tools policymakers and practitioners use to evaluate and plan such systems' short, medium, and long-term evolution. The size and complexity of these models have evolved alongside their real-world counterparts to the point that they pose tractability problems that hinder their usefulness and suitability for extracting actionable insights. One of the main challenges in these models arises from their temporal structure, which makes them harder to solve as it exponentially increases the number of variables in the model; to overcome this, researchers developed temporal aggregation techniques which simplify the temporal structure of the model like representative periods and temporal downsampling, thus increasing the model's tractability. These techniques, however, come at the expense of losing sight of the interaction between short and long-term dynamics that play a critical role in the real world, like ramping or the intra-day behavior of renewable energy sources. In this work, we extend the Basis-Oriented Time Series Aggregation procedure to network flow problems and show how it greatly aggregates the model while maintaining its objective function value.

This study is in the context of the design of multi-energy microgrids under uncertainty, where the objective is to determine optimal sizes for renewable generators and flexibility assets considering stochastic parameters, such as energy demand and renewable energy sources availability.

In particular, we compare existing models to generate synthetic scenarios of these parameters leveraging historical data, e.g. using Markov Chains, probability distributions, and time series analysis. The assessment focuses on their ability to generate diverse scenarios that capture key characteristics of the original data. Additionally, we conduct an application-specific assessment to examine the impact of different scenario generation methods on design optimization. This evaluation utilizes a two-stage stochastic programming approach and a three-year dataset to evaluate performance on unseen scenarios.

Robust design of microgrids is a complex optimization process requiring multiple simulations in order to integrate uncertainty variables associated with the system environment or design models. In this context, having sufficiently accurate models that are compatible with the optimization algorithms and associated computational costs represents a real challenge. In this paper, we illustrate this through the robust design of a simple microgrid with electrochemical storage. Based on battery models that couple energy efficiency and aging, we develop an approach for choosing the right level of precision to match the microgrid's optimization criteria or constraints.

This paper focuses on a methodology developed to evaluate innovative control approaches for optimizing energy management in the context of energy flexibility, rising energy consumption, and thermal discomfort. To allow the development and testing of this evaluation methodology, various architectures of Model Predictive Controllers, which are renowned for their ability to address these challenges effectively, have been implemented. To assess the impact of predictive control on energy systems management optimization, the methodology is based on a multi-objective set of KPIs. Therefore, several performance indicators are defined and implemented to evaluate the controllers' effectiveness. Evaluations are carried out by integrating the controllers with a bottom-up dynamic simulation platform dedicated to district energy calculations, as an emulation tool. This offers, at last, an architecture and a methodology for comparing the performances of controllers, especially model predictive controllers.

Recent studies in Italy have shown that Renewable Energy Communities (RECs) can provide a viable and economically beneficial alternative to traditional energy supply. In addition, the 2019 EU directive mandates that the share of energy from renewable sources will be at least 32% by 2030. As Hungary is currently in the process of developing REC regulations, this work aims to devise an optimal energy management scheme for RECs, tailored to the Hungarian economic and infrastructural environment. The scheme is based on an optimal energy management framework utilizing mixed integer linear programming (MILP) to control a community energy storage system. The scheme is extended to take into account the unique consumption patterns in Hungary, especially the high penetration of controlled loads. In Hungary, controlled loads are managed by the electricity provider through demand-side management techniques, allowing partial control over their operation to effectively manage peak hours. This article highlights the benefits of utilizing load control in REC energy management, increasing the flexibility of the REC and as a result, leading to reduced optimal community battery size. To assess the results, a case study of four low-voltage Hungarian transformer areas is conducted. The optimal ratio of photovoltaic penetration and community battery size is determined, and economic key performance indicators are evaluated to assess the financial viability of these communities.

This talk analyzes different optimization models for evaluating investments in Energy Storage Systems (ESS) in power systems with high penetration of Renewable Energy Sources (RES). First of all, two methodologies proposed in the literature are extended. The enhanced models are the ‘System States Reduced Frequency Matrix' model, and the ‘Enhance Representative Periods’ model which guarantees some continuity between representative periods, e.g. days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as a benchmark. While the system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The enhanced representative period model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

The charging of electrical vehicles is often limited by electrical grid infrastructural constraints: the larger the number of electrical vehicles the higher the value of electrical energy needed to charge them and, in a given time interval, the power requested to the grid. An optimal strategy can distribute over time the charging sessions so that the grid peak power is reduced. The present work defines the constraints of an electrical bus charging system and presents two optimization strategies: one based on heuristic strategy and another on Mixed Integer Linear Programming. The two strategies can be used alternatively or in sequence: the first providing a starting point for the second. The optimal procedures are applied to the case of charging system for a fleet of electrical buses and some preliminary results are presented.

It is well-known that real-world topology optimisation problems often exhibit multiple local minima and that derivative-based methods are prone to getting stuck in a possibly suboptimal local solution. We present a way of computing multiple locally optimal solutions to topology optimisation and illustrate the method for a two-pole synchronous reluctance machine.

Topology and parameter optimization of IPM motors can be used to design motor shapes. In this paper, an IPM motor with high torque and high mechanical strength is designed by incorporating stress and connection constraints in addition to electromagnetic field analysis. The multi-objective topology optimization was performed, and feasible Pareto solutions were obtained.

The present paper proposes a methodology based on topology optimization to design a synchro-reluctant motor considering magneto-elastic coupling. Stress on the rotor related to inertia forces and press-fitting effect on the stator are considered. The magneto-mechanical coupling is taken into account using a simplified magneto-elastic analytical model for the material behavior law. The objective is to maximize the machine's torque while considering an equality constraint on the volume. The topology optimization approach is based on a discrete BESO algorithm.

This paper proposes a generalized multi-material filtering formalism in density-based topology optimization. This work’s novelty is the consideration of the periodic and anti-periodic boundary conditions commonly used to simulate electrical machines, which change the nature of the materials located outside the simulation zone. It affects the filtering, which relies on a convolutive averaging of the materials’ properties near the boundaries. The implementation is detailed and applied to the topology optimization of a permanent magnet machine rotor.

We investigate a PDE constrained design optimization problem with an uncertain
parameter. By utilizing a robust worst case formulation we obtain an optimization problem of
bi-level structure. To obtain tractability we approximate the worst case function with a linear
Taylor expansion. Numerical results are presented for validation.

In the present work we propose a different approach to simulate and optimize a
rotating electric motor. We solve the eddy-current equation, with the use of the shape derivative
and space-time finite element methods. We then also consider electro-thermal coupling.

This presentation considers the problems of designing passive devices for mode conversion and multiplexing in the context of acoustic and electromagnetic wave propagation using topology optimization. Although these problems may appear similar, this talk focuses on the differences between the two cases and, in particular, the unique features of the electromagnetic problem.

This work presents topological optimization method to design magnetic cricuit in 3D. This method is based on adjoint method using the equations of volume integral methods. Thus, our approach makes it possible to reduce the computational time and the memory compared to classical topology optimization methods based on finite element calculations.

In this paper, density-based topological optimization is used to design a simple magnetic circuit. This study aims to minimize the mechanical compliance of a circuit subjected to a surface force and under a constraint in order to generate a specific magnetic field in a target zone. We propose an approach based on the SIMP approach and the adjoint method to solve the topological optimization problem applied to design 3D magnetic circuit.

This work proposes a method to perform multi-objective optimization of inductors using a surrogate model based on neural network (NN) instead of finite element method (FEM). Traditional design methods require repetitive FEM calculations, resulting in a very long overall design time. In contrast, this work utilizes a well-trained neural network to predict magnetic core loss, the volume and saturation current, avoiding repetitive FEM evaluations and significantly reducing the total time for inductor optimization.

The possibility of adopting data-driven procedure to create a model of electromagnetic problems has been investigated since long. Recently, the availability of high-performance computing systems even at desktop level has provided different model of “artificial intelligence” processors able to deal with such a problem, examples being Physically-Informed Neural Networks, Generative Adversarial Networks. Etc. In this study, using a simple yet representative benchmark problem, some of the most common solutions are compared, with the aim of highlighting respective advantages and drawbacks.

This article propose an optimal design of control gain parameters using a Cauer ladder network with constant basis functions and an Impulse-response model based V-Tiger. We model the TEAM Workshop Problem 28 by the Cauer ladder network method, which is a model order reduction technique, and enable the analysis by a circuit simulator such as Simulink. We determine the control gain parameters by applying the Impulse-response model based V-Tiger to the data obtained from the analysis. This method is useful for efficiently determining control parameters on simulation.

This paper discusses a novel technique for quasi-global parameter tuning of high-frequency structures using response features and inverse surrogate models. Our approach enables a low-cost identification of the most promising parameter space regions, followed by a fine tuning by means of local routines. The presented method is validated using several high-frequency structures. Its global search capability and computational efficiency are demonstrated by extensive comparisons with multiple-start local search as well as nature-inspired optimizers.

This paper presents a novel multi-material topology optimization method for multi-segmented permanent magnet motors. In the proposed method, optimization process is divided into two stages. In the first step, multi-material topology optimization, in which magnetization vector of each magnet element is defined stochastically, is performed to determine the rough magnet arrangement. In the second step, the geometry obtained in the first step is used as the initial solution, and detailed design is performed. To validate the effectiveness, the proposed method is applied to the shape optimization problem of a multi-segmented permanent magnet motor.

This paper presents a topology optimization of on-chip planar inductors based on evolutional on/off method and CMA-ES. The conductor shape of the inductor is expressed through the spatially-smooth function and its variables are optimized by CMA-ES. It is shown that the resultant inductor shape is varied depending on the given specifications.

In this paper, topology optimization and the frozen permeability method is used to verify the effect of asymmetric flux barriers on torque ripple reduction. Although an interior permanent magnet motor with an asymmetric flux barrier has been proposed in previous studies, the number of references is limited and the mechanism is unclear. In this paper, the geometry of symmetric and asymmetric flux barriers is optimized using topology optimization. Torque analysis is then performed for the obtained optimized geometry, and the frozen permeability method is used to separate the cogging torque and the load torque. The torque separation results reveal the torque ripple reduction effect due to offsetting of cogging and load torque, which has not been reported in previous studies.

We derive surface integrals for calculating the Lorentz force acting on a non-magnetic
conductive specimen when moving in the field of a spherical permanent magnet. The integrals
are valid for any geometry of the specimen, moving direction, position, and orientation of the
magnet. We evaluate the performance of this approach on a thin and thick cuboid, thin disc,
sphere, and a thin cuboid containing a surface defect. The normalized root mean square errors
are below 0.4% with respect to a reference finite-element solution.

The reconstruction of active sources in the human brain based on electroencephalography (EEG) is challenging in many respects. For high-quality clinical source reconstruction good signal quality, individual head images for realistic volume conductor models, as well as complex software tools for data processing are needed. However, there is an increasing demand for mobile EEG applications and, thus, the need for source reconstruction in areas like sports science or rehabilitation. In this study, we explored the possibility of using dry EEG electrodes for source reconstruction applying an averaged head model for forward computation, and developed an easy-to-use MS-Windows-based data processing pipeline in Python. Right-hand motor execution (ME) and imagination (MI) EEG data, which are typically used in brain-computer interface scenarios, were used to test our pipeline. Preliminary results show enhanced source activity over the contralateral motor cortex for both, ME and MI. We could demonstrate that source localization is feasible for mobile dry EEG scenarios such as in rehabilitation applications.

The magnetization of commercial permanent magnets is uneven, and the spatial magnetic field is distorted by individual differences. Therefore, it is necessary to measure the magnetic field of each magnet, but measuring the three-dimensional magnetic field distribution takes time. Thus, we measured the magnetic field near the permanent magnet and estimated the spatial magnetic field using two methods: the Gaussian process regression method and the truncated singular value decomposition method. We also calculated the average error for the measured values in two methods. As a result, we found that the average error of the truncated singular value decomposition method was smaller than that of the Gaussian process regression method.

The main goal of this paper is to analyse the influence of the shape and duration of the excitation pulse on the quality of reconstructed images in the inverse problem of the magnetoacoustic tomography with magnetic induction (MAT-MI). To solve this problem, the obtained reconstruction images were subjected to a binarization process, and their quality in relation to the original image was determined using a correlation and PSNR indicators. The research was conducted based on the reconstruction results obtained for several different shapes and durations of the excitation pulse.

In accelerator systems, magnetic field analysis incorporating the effect of magnetic hysteresis is essential for achieving high efficiency in beam commissioning using electromagnets. The play model is one of the hysteresis models that uses a shape function generated from measured BH loops as input. However, numerous BH loops are needed to account for minor loops, such as those caused by beam misalignment of accelerator magnets, and these measurements are time-consuming. Therefore, we aimed to develop a beam commissioning method that utilizes the play model by interpolating arbitrary hysteresis loops from measured data using Gaussian process regression in combination with the reduced play model which we have already proposed.

This paper presents novel procedure for optimizing the shape of conductive shields for low and medium frequency magnetic fields using convolutional neural networks (CNNs). In general, shape optimization is a specific class of so called ill-posed inverse problems, because objectives have typically many local minima when varying shapes. In the first approach a CNN is used as a surrogate model of the forward problem, while in the second approach a CNN is trained to solve the inverse problem directly. The case study presented in this paper involves the shape design of an electromagnetic shield placed in a sinusoidal uniform magnetic field under given constraints.

This paper first presents analytical expressions for the effectiveness of shielding time-harmonic magnetic fields by means of a rotating double-shell cylindrical shield made of non-magnetic material. Next, the procedure is described for finding the optimal distance between the two shells and their thickness (the same for both shells) for the maximum reduction of the weight of the double-shell shield (which is equivalent to reducing the active cross-sectional area) compared to a thick single shield for a given value of the shielding factor.

Magnetic particle-based targeted drug delivery is gaining momentum in recent years.
Significant challenges remain when it comes to modelling the movement of particles and achieving
precise targeting to desired regions. In this study, we address these challenges by training a
data-driven model to accurately predict particle velocity from magnetic particle positions and
electromagnet currents in an in-vitro targeting setup. The model is successfully applied to a
control algorithm to actuate particles from an initial to a predefined final position.

In recent years, the development of electric vehicles is being actively pursued. Several permanent magnet synchronous motors are installed in xEVs. To improve the manufacturing quality of PM motors, it is essential for revealing the magnetization state inside PMs at the early design stage. Therefore, a method called SiGrad has been proposed to numerically estimate the magnetization distribution using the magnetic flux density measured around PMs. As a result, the effective performance of SiGrad is illustrated in the estimation problem of PM with the homogeneous magnetization. However, its performance regarding the estimation of inhomogeneous distribution with demagnetized region is not investigated. In this paper, the applicability of SiGrad to the demagnetized magnetization estimation is verifed, and Pinching-type SiGrad (P-Sigrad) is proposed to enhance the estimation accuracy of SiGrad.

In recent years, because the carbon neutral evokes the widespread of electrification, the demand of electric vehicles (xEV) is increasingly growing. The DC-DC converter loaded on xEV generates electromagnetic noise (EM) that can interfere with other equipment. Therefore, an effective electromagnetic shielding around the wire harness connected to the DC-DC converter is required by the design optimization method. In this paper, to establish the design method based on the topology optimization, the accuracy of the sensitivity analysis using the time domain adjoint variable method is investigated in the electromagnetic shielding model.

The reconstruction of the spatial complex conductivity σ + jωε0εr from complex valued impedance measurements forms the inverse problem of complex electrical impedance tomography, or complex electrical capacitance tomography, respectively. Regularized Gauß-Newton schemes have been proposed for their solution. However, the necessary computation of the Jacobian is known to be computationally expensive, as standard techniques such as adjoint field methods require additional simulations. In this work we show a more efficient way to computationally access the Jacobian matrix. In particular the presented techniques do not require additional simulations, making the use of the Jacobian free of additional computational costs.

An automatic design optimization of a wireless power transfer system is performed using Monte Carlo tree search. Several key factors, i.e., the compensation network, shapes and geometrical parameters of the core are determined after searches, in order to achieve the high transfer efficiencies for nonmisaligned and misaligned cases. This work demonstrates the effectiveness of Monte Carlo tree search in achieving design optimization for wireless power transfer systems.

The eddy current loss of stator windings in motor due to the skin and proximity effect cannot be ignored, especially when the input carrier harmonics increases. In this work, a semi-analytical homogenization method is used to calculate the impedance of a single tooth model in permanent-magnet synchronous motor. An optimization problem is solved to identify the circuit parameters in the Cauer circuit which has the impedance obtained by the homogenized analysis. The Cauer circuit can be used for a more effective time-domain analysis considering magnetic hysteresis.

This study proposes an optimization method for magnetic shielding in eddy current systems through the design of conductive shields using the level set method. The shape of the conductive shield was represented through the level set function, and the shape deformation was performed by solving the level set equation. The expression of the design velocity in the level set equation was defined based on the continuum sensitivity of the eddy current system to the conductive shield shape. The proposed method was verified by applying it to the conductive shielding design of the magnetic induction tomography system.

The design and optimization of the electromagnetic layout of E-Machines in electric vehicles play a crucial role in achieving high-performance and efficient propulsion systems. This abstract paper explores the significance of optimization techniques in the layout of electromagnetic components within electric vehicle (EV) powertrains, with a specific emphasis on addressing noise, vibration, and harshness (NVH) considerations.

Various aspects of the electromagnetic layout optimization are discussed, encompassing the geometric arrangement of motor components such as stator winding, rotor structure, and permanent magnets, as well as the selection of suitable materials. The paper addresses the challenges associated with striking a balance between compact design, cooling efficiency, electromagnetic performance, NVH characteristics and cost . An emphasis is placed on conducting multi-objective optimizations to obtain optimal E-Machines that excel in performance and NVH metrics.

The utilization of advanced simulation tools and optimization algorithms is explored, providing engineers with the means to model and analyze electromagnetic characteristics, evaluate design alternatives, and identify areas for improvement. The paper highlights the benefits of simulation-based optimization, which include reduced development time, cost savings, and enhanced design accuracy.

Furthermore, the paper presents case studies and real-world examples where optimization techniques have been successfully applied to the electromagnetics layout of E-Machines in electric vehicles. These examples illustrate the practical implementation of optimization methods and their impact on improving motor efficiency, reducing losses, and enhancing overall vehicle performance while considering NVH aspects.

Sensitivity analysis (SA) is performed in this work, including various parameters and
considering different transmitting (TX) antenna types in reverberation chambers (RCs). To this
end, surrogate modeling techniques were involved to efficiently calculate the Sobol’ indices as a
measure of uncertainty quantification (UQ). This approach helps to appraise the contributions
of different parameters in the lower frequency range, where the well-stirred condition cannot be
established, yielding a proficient apparatus for the stirrer and the chamber design.

Parasitic electromagnetic (EM) effects within passive components are a fundamental issue in EMI filter applications when certain filter specifications must be met. In this work, a surrogate model based optimization methodology is used to identify an ideal geometry of a surface-mounted (SMT) ferrite bead to reduce its parasitic capacitance while a maximal inductance and consequently a maximal impedance is provided. The identified geometry exhibits an ideal device behavior up to 1 GHz.

In this work, two different approaches for solving an inverse problem to determine the local magnetic material properties of electrical steel sheets are compared. The first approach involves a quasi Newton method approximating the Jacobian with Broyden's update formula and the second is an adjoint method. To handle the ill-posedness of the inverse problem, a Thikonov regularization is used for both methods and the regularization parameter is computed via Morozov's discrepancy principle.

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 as well as the positions of the system relative to the steel sheets should be improved in order to increase the identifiability of the material distribution. For the objective function of this design optimization the Fisher information matrix (FIM) is used, which allows to quantify the amount of information that the measurements carry about the unknown parameters. The forward problem is solved by the finite element method.

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.