Electric Networks
From the fully automatic coffee machine to the central processing units, electronic circuits and computer chips enhance the quality of our daily life. Since these circuits consist of millions of network elements, like resistors, capacitors, inductors and transistors, chip design relies strongly on circuit simulation. The computed electric behavior is used by the designer to rate and validate new developements before production.
Miniaturization of single elements and ultra high integration is the ongoing trend to enhance the performance of all electronic devices. It leads to smaller devices driven by higher frequencies and smaller signals and overall it leads from micro- to nanotechnolgy.
In turn, modern circuit simulators have to face several challenges: higher packing densities result in larger systems with increased power density, and smaller signals result in larger noise/signal ratio and thus stronger influence of parasitic effects, which could have been neglected before.
This leads to our ongoing research in the field of circuit simulation:
- Speed up of circuit simulation by identification of active and latent parts in electric circuits (Multirate)
- Model Order Reduction for parasitic circuits
- Coupling of circuit simulators with distributed device models (Dynamic Iteration, Semiconductors, Electromagnetic Field Devices)
Former and ongoing projects
Cooperations
- Academic
- Markus Clemens, Bergische Universität Wuppertal
- Herbert de Gersem, K.U. Leuven, Belgium
- Caren Tischendorf, Universität zu Köln, Germany
- Industrial
Publications
- 2024
5361.
Kiesling, Elisabeth; Venzlaff, Julian; Bohrmann-Linde, Claudia
BNE-Fortbildungsreihe für Lehrkräfte und Studierende in der Didaktik der Chemie
Herausgeber: Gemeinsamer Studienausschuss (GSA) in der School of Education an der Bergischen Universität Wuppertal
Newsletter Lehrer*innenbildung an der Bergischen Universität Wuppertal
Juli 20245360.
Klass, Friedemann; Bartel, Andreas; Gabbana, PD Alessandro
Boundary conditions for multi-speed lattice Boltzmann methods
20245359.
Bailo, Rafael; Barbaro, Alethea; Gomes, Susana N.; Riedl, Konstantin; Roith, Tim; Totzeck, Claudia; Vaes, Urbain
CBX: Python and Julia Packages for Consensus-Based Interacting Particle Methods
Journal of Open Source Software, 9 (98) :6611
2024
Herausgeber: The Open Journal5358.
Fasi, Massimiliano; Gaudreault, Stéphane; Lund, Kathryn; Schweitzer, Marcel
Challenges in computing matrix functions
20245357.
Klass, Friedemann; Gabbana, Alessandro; Bartel, Andreas
Characteristic boundary condition for thermal lattice Boltzmann methods
Computers & Mathematics with Applications, 157 :195–208
2024
Herausgeber: Pergamon5356.
Klass, Friedemann; Gabbana, Alessandro; Bartel, Andreas
Characteristic boundary condition for thermal lattice Boltzmann methods
Computers & Mathematics with Applications, 157 :195-208
Juli 2024
ISSN: 0898-12215355.
Finster, Rebecca; Grogorick, Linda; Robra-Bissantz, Susanne
ChatGPT erzähl mir eine Geschichte: Die Verwandlung von Lernwelten durch KI-gestützte Erzählungen
DeLFI Fachtagung Bildungstechnologien
Fulda
2024ISBN: 978-3-88579-255-0
5354.
Yoda, R.; Bolten, M.; Nakajima, K.; Fujii, A.
Coarse-grid operator optimization in multigrid reduction in time for time-dependent Stokes and Oseen problems
Jpn. J. Ind. Appl. Math.
20245353.
Abel, Ulrich; Acu, Ana Maria; Heilmann, Margareta; Raşa, Ioan
Commutativity and spectral properties for a general class of Szász-Mirakjan-Durrmeyer operators
Advances in Operator Theory, 10 (1) :14
20245352.
Vorberg, Lukas; Jacob, Birgit; Wyss, Christian
Computing the Quadratic Numerical Range
Journal of Computational and Applied Mathematics :116049
20245351.
Klamroth, Kathrin; Stiglmayr, Michael; Totzeck, Claudia
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
Journal of Global Optimization
20245350.
Günther, Michael; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Mathematics of Control, Signals, and Systems, 36 (4) :957–977
2024
Herausgeber: Springer London5349.
Günther, Michael; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Mathematics of Control, Signals, and Systems, 36 (4) :957–977
2024
Herausgeber: Springer London5348.
Günther, M.; Jacob, B.; Totzeck, C.
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Math. Control Signals Syst., 36 :957–977
20245347.
Günther, M.; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Math. Control Signals Syst.
20245346.
Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes
Preprint
20245345.
Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes
Preprint
20245344.
Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes.
20245343.
Kapllani, Lorenc; Teng, Long
Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations
Discrete and continuous dynamical systems - B, 29 (4) :1695–1729
2024
Herausgeber: AIMS Press5342.
Ackermann, Julia; Jentzen, Arnulf; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations
arXiv:2406.10876 :64 pages
20245341.
Kossaczká, Tatiana; Jagtap, Ameya D; Ehrhardt, Matthias
Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators
Physics of Fluids, 36 (3)
2024
Herausgeber: AIP Publishing5340.
Kossaczká, Tatiana; Jagtap, Ameya D; Ehrhardt, Matthias
Deep smoothness WENO method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators
Physics of Fluid, 36 (3) :036603
2024
Herausgeber: AIP Publishing5339.
Kossaczká, Tatiana; Jagtap, Ameya D; Ehrhardt, Matthias
Deep smoothness WENO method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators
Physics of Fluid, 36 (3) :036603
2024
Herausgeber: AIP Publishing5338.
Stiglmayr, Michael; Uhlemeyer, Svenja; Uhlemeyer, Björn; Zdrallek, Markus
Determining Cost-Efficient Controls of Electrical Energy Storages Using Dynamic Programming
Journal of Mathematics in Industry
20245337.
Ehrhardt, M.; Kruse, T.; Tordeux, A.
Dynamics of a Stochastic port-{H}amiltonian Self-Driven Agent Model in One Dimension
ESAIM: Math. Model. Numer. Anal.
2024