Magnetics

Computational Magnetics

Many electro-technical devices such as e.g. printed circuit boards, electrical drives and antenna systems can be simulated on the basis of electrical circuits. However, the increasing frequencies and the decreasing size force designers to account for wave propagation effects, eddy-current effects, ferromagnetic saturation and hysteresis. For wave propagation effects and eddy-current effects, the results of stand-alone field simulation can be represented by an order-reduced equivalent model, which is then inserted in the overall circuit model. The representation of field-dependent nonlinearities and hysteresis effects, however, is not straightforward.

The coupled field and circuit simulation becomes troublesome when a large number of time steps is required. This occurs when e.g. simulating an electrical drive where the machine requires 10 periods of 50 Hz to reach nominal speed whereas the switching of the Insulated Gate Bipolar Transistors in the frequency converter switches at 20 kHz, necessitating a time steps in the order of a microsecond to be used in the simulation. Since the field model consists typically of a few million degrees of freedom, all those unknowns have to be solved in every time step. Fortunately, the relevant time constants in electrical-energy converter are in the range 50 Hz. Hence the field model does not have to be time-stepped at the same rate as the circuit model, in which fast switches are present. The use of adaptive multirate time-integration schemes can reduce the numerical complexity of the problem substantially.

Research Questions

Efficiency of the time-integration for field devices in pulsed circuits (multirate, dynamic iteration)
DAE-index of the coupled system
Existence and Uniqueness of the solution

Cooperation

Herbert De Gersem, Katholieke Universiteit Leuven
Markus Clemens, Bergische Universität Wuppertal
Sascha Baumanns, Universität zu Köln

Former and ongoing projects

Publications

2023: 5190.
Jacob, Birgit; Mironchenko, Andrii; Partington, Jonathan R.; Wirth, Fabian
Corrigendum: Noncoercive Lyapunov functions for input-to-state stability of infinite-dimensional systems
SIAM J. Control Optim., 61 (2) :723-724
2023
DOI: 10.1137/22M1534559; 5189.
Aerdker, S.; others
CRPropa 3.2: a public framework for high-energy astroparticle simulations
PoS, ICRC2023 :1471
2023
DOI: 10.22323/1.444.1471; 5188.
Günther, Michael; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
arXiv preprint arXiv:2301.03924
2023; 5187.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier
DOI: 10.1016/j.fraope.2023.100039; 5186.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier
DOI: 10.1016/j.fraope.2023.100039; 5185.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier
DOI: 10.1016/j.fraope.2023.100039; 5184.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal
Download; 5183.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal
Download; 5182.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear Backward Stochastic Differential Equations
Discrete Contin. Dyn. Syst. - B
2023
ISSN: 1531-3492
DOI: 10.3934/dcdsb.2023151; 5181.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations
Discrete Contin. Dyn. Syst. - B
2023
DOI: 10.3934/dcdsb.2023151; 5180.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
2023
DOI: 10.48550/arXiv.2309.13722; 5179.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense
Preprint
2023
DOI: 10.48550/arXiv.2309.13722; 5178.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense
Preprint
2023
DOI: 10.48550/arXiv.2309.13722; 5177.
Abdul Halim, Adila; others
Deep-Learning-Based Cosmic-Ray Mass Reconstruction Using the Water-Cherenkov and Scintillation Detectors of AugerPrime
PoS, ICRC2023 :371
2023
DOI: 10.22323/1.444.0371; 5176.
Kowol, Philipp; Bargmann, Swantje; Görrn, Patrick; Wilmers, Jana
Delamination Behavior of Highly Stretchable Soft Islands Multi-Layer Materials
Applied Mechanics, 4 (2) :514--527
2023
ISSN: 2673-3161
DOI: 10.3390/applmech4020029
Download; 5175.
Khosrawi-Rad, Bijan; Borchers, Arne; Grogorick, Linda; Robra-Bissantz, Susanne
Design Principles for Gamified Pedagogical Conversational Agents
Seite 67 - 82
Academic MindTrek Conference (MindTrek)
Tampere, Finnland
2023
DOI: doi.org/10.1145/3616961.361697; 5174.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
2023
DOI: 10.48550/arXiv.2312.01448; 5173.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
2023
DOI: 10.48550/arXiv.2312.01448; 5172.
Felpel, Mike; Kienitz, Jörg; McWalter, Thomas
Effective stochastic local volatility models
Quantitative Finance, 23 (12) :1731–1750
2023
Herausgeber: Routledge
DOI: 10.1080/14697688.2023.2271514; 5171.
Klamroth, Kathrin; Lang, Bruno; Stiglmayr, Michael
Efficient Dominance Filtering for Unions and Minkowski Sums of Non-Dominated Sets
Computers and Operations Research
2023
Herausgeber: Elsevier {BV}
DOI: 10.1016/j.cor.2023.106506
Download; 5170.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI
DOI: 10.3390/appliedmath3020018; 5169.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI
DOI: 10.3390/appliedmath3020018; 5168.
Di Persio, Luca; Ehrhardt, Matthias
Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case
AppliedMath, 3 (2) :316--342
2023
Herausgeber: Multidisciplinary Digital Publishing Institute; 5167.
Glück, Jochen; Hölz, Julian
Eventual cone invariance revisited
Linear Algebra and its Applications, 675 :274 - 293
2023
DOI: https://doi.org/10.1016/j.laa.2023.06.014
Download; 5166.
Janssen, Nils; Fetzer, Jana R; Grewing, Jannis; Burgmann, Sebastian; Janoske, Uwe
Experimental investigation of particle--droplet--substrate interaction
Experiments in Fluids, 64 (3) :44
2023
Herausgeber: Springer Berlin Heidelberg Berlin/Heidelberg

Computational Magnetics

Research Questions

Cooperation

Former and ongoing projects

Publications

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