Multirate
Highly integrated electric cicuits show a phenomenon called latency. That is, a processed signal causes activity only in a small subset of the whole circuit (imagine a central processing unit), whereas the other part of the system behaves almost constant over some time - is latent. Such an electric system can be described as coupled system, where the waveforms show different time scales, also refered to as multirate.
More generally, any coupled problem formulation due to coupled physical effects, may cause a multirate problem: image the simulation of car driving on the road, there you need a model for the wheel, the chassis, the dampers, the road,... (cf. co-simulation). Again each system is covered by their own time constant, which might vary over several orders of magnitude comparing different subsystems.
Classical methods cannot exploit this multirate potential, but resolve everything on the finest scale. This causes an over sampling of the latent components. In constrast, Co-simulation or especially dedicated multirate methods are designed to use the inherent step size to resolve the time-domain behaviour of each subystem with the required accuracy. This requires a time-stepping for each.
Group members working in that field
- Andreas Bartel
- Michael Günther
Former and ongoing Projects
Cooperations
- Herbert de Gersem, K.U. Leuven, Belgium
- Jan ter Maten, TU Eindhoven and NXP, the Netherlands
Publications
- 2023
5193.
Abdul Halim, Adila; others
Constraints on UHECR characteristics from cosmogenic neutrino limits with the measurements of the Pierre Auger Observatory
PoS, ICRC2023 :1520
20235192.
Abdul Halim, Adila; others
Constraints on upward-going air showers using the Pierre Auger Observatory data
PoS, ICRC2023 :1099
20235191.
Acu, Ana-Maria; Heilmann, Margareta; Raşa, Ioan; Seserman, Andra
Convergence of linking Durrmeyer type modifications of generalized Baskakov operators
Bulletin of the Malaysian Math. Sciences Society, 46 (3)
20235190.
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
20235189.
Aerdker, S.; others
CRPropa 3.2: a public framework for high-energy astroparticle simulations
PoS, ICRC2023 :1471
20235188.
Günther, Michael; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
arXiv preprint arXiv:2301.03924
20235187.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier5186.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier5185.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier5184.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal5183.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal5182.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear Backward Stochastic Differential Equations
Discrete Contin. Dyn. Syst. - B
2023
ISSN: 1531-34925181.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations
Discrete Contin. Dyn. Syst. - B
20235180.
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
20235179.
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
20235178.
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
20235177.
Abdul Halim, Adila; others
Deep-Learning-Based Cosmic-Ray Mass Reconstruction Using the Water-Cherenkov and Scintillation Detectors of AugerPrime
PoS, ICRC2023 :371
20235176.
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: 2673-31615175.
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
20235174.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
20235173.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
20235172.
Felpel, Mike; Kienitz, Jörg; McWalter, Thomas
Effective stochastic local volatility models
Quantitative Finance, 23 (12) :1731–1750
2023
Herausgeber: Routledge5171.
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}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: MDPI5169.
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