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

5002.

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

5001.

Clément, François; Doerr, Carola; Klamroth, Kathrin; Paquete, Luís
Constructing Optimal Star Discrepancy Sets
2024

5000.

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

4999.

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

4998.

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

4997.

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

4996.

Fasi, Massimiliano; Gaudreault, Stéphane; Lund, Kathryn; Schweitzer, Marcel
Challenges in computing matrix functions
2024

4995.

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

4994.

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

4993.

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

4992.

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

4991.

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

4990.

Wiebel, Michelle; Bensberg, Kathrin; Wende, Luca; Grandrath, Rebecca; Plitzko, Kathrin; Bohrmann-Linde, Claudia; Kirsch, S. F.; Schebb, Nils Helge
Efficient and Simple Extraction Protocol for Triterpenic Acids from Apples
Journal of Chemical Education, 101 :2087-2093
April 2024
Publisher: ACS

4989.

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

4988.

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

4987.

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

4986.

Kruse, Thomas; Strack, Philipp
Optimal dynamic control of an epidemic
Operations Research, 72 (3) :1031–1048
2024
Publisher: INFORMS

4985.

Klass, Friedemann; Gabbana, Alessandro; Bartel, Andreas
Characteristic boundary condition for thermal lattice Boltzmann methods
Computers & Mathematics with Applications, 157 :195–208
2024
Publisher: Pergamon

4984.

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
2024

4983.

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

4982.

Clevenhaus, Anna; Totzeck, Claudia; Ehrhardt, Matthias
A numerical study of the impact of variance boundary conditions for the Heston model
In Burnecki, K. and Szwabiński, J. and Teuerle, M., Editor
Springer
In Burnecki, K. and Szwabiński, J. and Teuerle, M., Editor
Publisher: Bergische Universität Wuppertal
2024

4981.

Kapllani, Lorenc; Teng, Long
{A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations}
2024

4980.

Petrov, Pavel S.; Ehrhardt, Matthias; Kozitskiy, Sergey B.
A generalization of the split-step Padé method to the case of coupled acoustic modes equation in a 3D waveguide
Journal of Sound and Vibration, 577 :118304
2024
Publisher: Academic Press

4979.

Petrov, Pavel S; Ehrhardt, Matthias; Kozitskiy, Sergey B
A generalization of the split-step Padé method to the case of coupled acoustic modes equation in a 3D waveguide
Journal of Sound and Vibration :118304
2024
Publisher: Elsevier

4978.

Clevenhaus, Anna; Totzeck, Claudia; Ehrhardt, Matthias
A gradient-based calibration method for the Heston model
International Journal of Computer Mathematics, 101 (9-10) :1094–1112
2024
Publisher: Taylor & Francis

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