Applied and Computational Mathematics (ACM)

Coupled DAE Problems

A circuit (DAE model) coupled to a magnetostatic field device (PDE model)

Coupled Problems of differential-algebraic equations (DAEs) arise typically from either multiphysical modeling (e.g. in circuit simulation with heating) or from refined modeling, where crucial parts of the original problem are replaced by a better, but computational more expensive model (e.g. circuits refined by field models). Furthermore splitting methods may turn a monolithic DAE problem into coupled subproblems, e.g. because of different time scales (multirate). In any case the DAEs arise from network approaches or space-discretization of PDAEs (Partial Differential Algebraic Equations).

Often the coupled equations have quite different properties, i.e., symmetries, definiteness or time scales. Thus the coupled system must be analyzed (e.g. the index) and tailored methods have to be developed (e.g. dynamic iteration).

Details

Publications



2024

5359.

Klamroth, Kathrin; Stiglmayr, Michael; Totzeck, Claudia
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
Journal of Global Optimization
2024

5358.

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
Publisher: Springer London

5357.

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
Publisher: Springer London

5356.

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
2024

5355.

Günther, M.; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
Math. Control Signals Syst.
2024

5354.

Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes
Preprint
2024

5353.

Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes
Preprint
2024

5352.

Zaspel, Peter; Günther, Michael
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes.
2024

5351.

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
Publisher: AIMS Press

5350.

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
2024

5349.

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
Publisher: AIP Publishing

5348.

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
Publisher: AIP Publishing

5347.

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
Publisher: AIP Publishing

5346.

Stiglmayr, Michael; Uhlemeyer, Svenja; Uhlemeyer, Björn; Zdrallek, Markus
Determining Cost-Efficient Controls of Electrical Energy Storages Using Dynamic Programming
Journal of Mathematics in Industry
2024

5345.

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

5344.

Schlimbach, Ricarda; Richter, Janine; Grogorick, Linda
Embarking on an Interactive Learning Journey: Exploring the Interaction Value of Voicebots vs. Chatbots
European Conference on Information Systems (ECIS)
Paphos, Cyprus
2024

5343.

Santos, Daniela Scherer; Klamroth, Kathrin; Martins, Pedro; Paquete, Luís
Ensuring connectedness for the Maximum Quasi-clique and Densest $k$-subgraph problems
2024

5342.

Holzenkamp, Matthias; Lyu, Dongyu; Kleinekathöfer, Ulrich; Zaspel, Peter
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.
2024

5341.

Gaul, Daniela
Exact and Heuristic Methods for Dial-a-Ride Problems
Dissertation
Dissertation
Bergische Universität Wuppertal
2024

5340.

Lyu, Dongyu; Holzenkamp, Matthias; Vinod, Vivin; Holtkamp, Yannick M.; Maity, Sayan; Salazar, Carlos R.; Kleinekathöfer, Ulrich; Zaspel, Peter
Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning.
2024

5339.

Kienitz, Jörg
Exciting times are ahead - Gaussian views and yield curve extrapolation
Wilmott, 2024 (134) :46–50
2024
Publisher: Wilmott Magazine

5338.

[german] Zeller, Diana; Bohrmann-Linde, Claudia
Falschinformationen in Videos? Mit dem Konzept KriViNat die Kompetenz der Informationsbewertung stärken
In Bohrmann-Linde, C.; Gökkus, Y.; Meuter, N.; Zeller, D., Editor, Volume Netzwerk Digitalisierter Chemieunterricht. Sammelband NeDiChe-Treff 2022
Page 9-15
Publisher: Chemiedidaktik. Bergische Universität Wuppertal
2024
9-15

5337.

Abel, Ulrich; Acu, Ana-Maria; Heilmann, Margareta; Raşa, Ioan
Genuine Bernstein-Durrmeyer operators preserving 1 and x^j (II)
ATSF 2024
Ankara, Turkey
2024

5336.

Bartel, Andreas; Schaller, Manuel
Goal-oriented time adaptivity for port-{H}amiltonian systems
2024

5335.

Schäfers, Kevin; Finkenrath, Jacob; Günther, Michael; Knechtli, Francesco
Hessian-free force-gradient integrators
2024